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1
Fathi, Yahya, and Craig Tovey. "Affirmative action algorithms." Mathematical Programming 34, no. 3 (April 1986): 292–301. http://dx.doi.org/10.1007/bf01582232.
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Gite, Shilpa, and Himanshu Agrawal. "Early Prediction of Driver's Action Using Deep Neural Networks." International Journal of Information Retrieval Research 9, no. 2 (April 2019): 11–27. http://dx.doi.org/10.4018/ijirr.2019040102.
Повний текст джерелаАнотація:
Intelligent transportation systems (ITSs) are one of the most widely-discussed and researched topic across the world. The researchers have focused on the early prediction of a driver's movements before drivers actually perform actions, which might suggest a driver to take a corrective action while driving and thus, avoid the risk of an accident. This article presents an improved deep-learning technique to predict a driver's action before he performs that action, a few seconds in advance. This is considering both the inside context (of the driver) and the outside context (of the road), and fuses them together to anticipate the actions. To predict the driver's action accurately, the proposed work is inspired by recent developments in recurrent neural networks (RNN) with long short term memory (LSTM) algorithms. The performance merit of the proposed algorithm is compared with four other algorithms and the results suggest that the proposed algorithm outperforms the other algorithms using a range of performance metrics.
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Wu, Songjiao. "Image Recognition of Standard Actions in Sports Videos Based on Feature Fusion." Traitement du Signal 38, no. 6 (December 31, 2021): 1801–7. http://dx.doi.org/10.18280/ts.380624.
Повний текст джерелаАнотація:
Standard actions are crucial to sports training of athletes and daily exercise of ordinary people. There are two key issues in sports action recognition: the extraction of sports action features, and the classification of sports actions. The existing action recognition algorithms cannot work effectively on sports competitions, which feature high complexity, fine class granularity, and fast action speed. To solve the problem, this paper develops an image recognition method of standard actions in sports videos, which merges local and global features. Firstly, the authors combed through the functions and performance required for the recognition of standard actions of sports, and proposed an attention-based local feature extraction algorithm for the frames of sports match videos. Next, a sampling algorithm was developed based on time-space compression, and a standard sports action recognition algorithm was designed based on time-space feature fusion, with the aim to fuse the time-space features of the standard actions in sports match videos, and to overcome the underfitting problem of direct fusion of time-space features extracted by the attention mechanism. The workflow of these algorithms was explained in details. Experimental results confirm the effectiveness of our approach.
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Mudge, Michael E., and J. P. Killingbeck. "Microcomputer Algorithms: Action for Algebra." Mathematical Gazette 76, no. 476 (July 1992): 305. http://dx.doi.org/10.2307/3619164.
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Abdallah, S., and V. Lesser. "A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics." Journal of Artificial Intelligence Research 33 (December 17, 2008): 521–49. http://dx.doi.org/10.1613/jair.2628.
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Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Due to the complexity of the problem, the majority of the previously developed MARL algorithms assumed agents either had some knowledge of the underlying game (such as Nash equilibria) and/or observed other agents actions and the rewards they received. We introduce a new MARL algorithm called the Weighted Policy Learner (WPL), which allows agents to reach a Nash Equilibrium (NE) in benchmark 2-player-2-action games with minimum knowledge. Using WPL, the only feedback an agent needs is its own local reward (the agent does not observe other agents actions or rewards). Furthermore, WPL does not assume that agents know the underlying game or the corresponding Nash Equilibrium a priori. We experimentally show that our algorithm converges in benchmark two-player-two-action games. We also show that our algorithm converges in the challenging Shapley's game where previous MARL algorithms failed to converge without knowing the underlying game or the NE. Furthermore, we show that WPL outperforms the state-of-the-art algorithms in a more realistic setting of 100 agents interacting and learning concurrently. An important aspect of understanding the behavior of a MARL algorithm is analyzing the dynamics of the algorithm: how the policies of multiple learning agents evolve over time as agents interact with one another. Such an analysis not only verifies whether agents using a given MARL algorithm will eventually converge, but also reveals the behavior of the MARL algorithm prior to convergence. We analyze our algorithm in two-player-two-action games and show that symbolically proving WPL's convergence is difficult, because of the non-linear nature of WPL's dynamics, unlike previous MARL algorithms that had either linear or piece-wise-linear dynamics. Instead, we numerically solve WPL's dynamics differential equations and compare the solution to the dynamics of previous MARL algorithms.
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Yang, Jianhua. "A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/2663834.
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In sports, the essence of a complete technical action is a complete information structure pattern and the athlete’s judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictive response information when it identifies and confirms it according to the constantly changing motion information on the field. Action recognition mainly includes two aspects: one is to obtain the required action information based on visual observation and the other is to judge the action based on the obtained action information, but the neuropsychological mechanism of this process is still unknown. In this paper, a new key frame extraction method based on the clustering algorithm and multifeature fusion is proposed for sports videos with complex content, many scenes, and rich actions. First, a variety of features are fused, and then, similarity measurement can be used to describe videos with complex content more completely and comprehensively; second, a clustering algorithm is used to cluster sports video sequences according to scenes, eliminating the need for shots in the case of many scenes. It is difficult and complicated to detect segmentation; third, extracting key frames according to the minimum motion standard can more accurately represent the video content with rich actions. At the same time, the clustering algorithm used in this paper is improved to enhance the offline computing efficiency of the key frame extraction system. Based on the analysis of the advantages and disadvantages of the classical convolutional neural network and recurrent neural network algorithms in deep learning, this paper proposes an improved convolutional network and optimization based on the recognition and analysis of human actions under complex scenes, complex actions, and fast motion compared to post-neural network and hybrid neural network algorithm. Experiments show that the algorithm achieves similar human observation of athletes’ training execution and completion. Compared with other algorithms, it has been verified that it has very high learning rate and accuracy for the athlete’s action recognition.
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Abduljabbar Ali, Mohammed, Abir Jaafar Hussain, and Ahmed T. Sadiq. "Deep Learning Algorithms for Human Fighting Action Recognition." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 02 (February 16, 2022): 71–87. http://dx.doi.org/10.3991/ijoe.v18i02.28019.
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— Human action recognition using skeletons has been employed in various applications, including healthcare robots, human-computer interaction, and surveillance systems. Recently, deep learning systems have been used in various applications, such as object classification. In contrast to conventional techniques, one of the most prominent convolutional neural network deep learning algorithms extracts image features from its operations. Machine learning in computer vision applications faces many challenges, including human action recognition in real time. Despite significant improvements, videos are typically shot with at least 24 frames per second, meaning that the fastest classification technologies take time. Object detection algorithms must correctly identify and locate essential items, but they must also be speedy at prediction time to meet the real-time requirements of video processing. The fundamental goal of this research paper is to recognize the real-time state of human fighting to provide security in organizations by discovering and identifying problems through video surveillance. First, the images in the videos are investigated to locate human fight scenes using the YOLOv3 algorithm, which has been updated in this work. Our improvements to the YOLOv3 algorithm allowed us to accelerate the exploration of a group of humans in the images. The center locator feature in this algorithm was adopted as an essential indicator for measuring the safety distance between two persons. If it is less than a specific value specified in the code, they are tracked. Then, a deep sorting algorithm is used to track people. This framework is filtered to process and classify whether these two people continue to exceed the programmatically defined minimum safety distance. Finally, the content of the filter frame is categorized as combat scenes using the OpenPose technology and a trained VGG-16 algorithm, which classifies the situation as walking, hugging, or fighting. A dataset was created to train these algorithms in the three categories of walking, hugging, and fighting. The proposed methodology proved successful, exhibiting a classification accuracy for walking, hugging, and fighting of 95.0%, 87.4%, and 90.1%, respectively.
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Amir, E., and A. Chang. "Learning Partially Observable Deterministic Action Models." Journal of Artificial Intelligence Research 33 (November 20, 2008): 349–402. http://dx.doi.org/10.1613/jair.2575.
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We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model(the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and action models. In particular, it focuses on problems in which actions are deterministic of simple logical structure and observation models have all features observed with some frequency. We yield tractable algorithms for the modified problem for such domains. Our algorithms take sequences of partial observations over time as input, and output deterministic action models that could have lead to those observations. The algorithms output all or one of those models (depending on our choice), and are exact in that no model is misclassified given the observations. Our algorithms take polynomial time in the number of time steps and state features for some traditional action classes examined in the AI-planning literature, e.g., STRIPS actions. In contrast, traditional approaches for HMMs and Reinforcement Learning are inexact and exponentially intractable for such domains. Our experiments verify the theoretical tractability guarantees, and show that we identify action models exactly. Several applications in planning, autonomous exploration, and adventure-game playing already use these results. They are also promising for probabilistic settings, partially observable reinforcement learning, and diagnosis.
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Christiansen, Alan D., and Kenneth Y. Goldberg. "Comparing two algorithms for automatic planning by robots in stochastic environments." Robotica 13, no. 6 (November 1995): 565–73. http://dx.doi.org/10.1017/s0263574700018646.
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SummaryPlanning a sequence of robot actions is especially difficult when the outcome of actions is uncertain, as is inevitable when interacting with the physical environment. In this paper we consider the case of finite state and action spaces where actions can be modeled as Markov transitions. Finding a plan that achieves a desired state with maximum probability is known to be an NP-Complete problem. We consider two algorithms: an exponential-time algorithm that maximizes probability, and a polynomial-time algorithm that maximizes a lower bound on the probability. As these algorithms trade off plan time for plan quality, we compare their performance on a mechanical system for orienting parts. Our results lead us to identify two properties of stochastic actions that can be used to choose between these planning algorithms for other applications.
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Wu, Yuchuan, Shengfeng Qi, Feng Hu, Shuangbao Ma, Wen Mao, and Wei Li. "Recognizing activities of the elderly using wearable sensors: a comparison of ensemble algorithms based on boosting." Sensor Review 39, no. 6 (November 18, 2019): 743–51. http://dx.doi.org/10.1108/sr-11-2018-0309.
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Purpose In human action recognition based on wearable sensors, most previous studies have focused on a single type of sensor and single classifier. This study aims to use a wearable sensor based on flexible sensors and a tri-axial accelerometer to collect action data of elderly people. It uses a statistical modeling approach based on the ensemble algorithm to classify actions and verify its validity. Design/methodology/approach Nine types of daily actions were collected by the wearable sensor device from a group of elderly volunteers, and the time-domain features of the action sequences were extracted. The dimensionality of the feature vectors was reduced by linear discriminant analysis. An ensemble learning method based on XGBoost was used to build a model of elderly action recognition. Its performance was compared with the action recognition rate of other algorithms based on the Boosting algorithm, and with the accuracy of single classifier models. Findings The effectiveness of the method was validated by three experiments. The results show that XGBoost is able to classify nine daily actions of the elderly and achieve an average recognition rate of 94.8 per cent, which is superior to single classifiers and to other ensemble algorithms. Practical implications The research could have important implications for health care, including the treatment and rehabilitation of the elderly, and the prevention of falls. Originality/value Instead of using a single type of sensor, this research used a wearable sensor to obtain daily action data of the elderly. The results show that, by using the appropriate method, the device can obtain detailed data of joint action at a low cost. Comparing differences in performance, it was concluded that XGBoost is the most suitable algorithm for building a model of elderly action recognition. This method, together with a wearable sensor, can provide key data and accurate feedback information to monitor the elderly in their rehabilitation activities.
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Huang, Pan, Yanping Li, Xiaoyi Lv, Wen Chen, and Shuxian Liu. "Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM." Sensors 20, no. 5 (March 6, 2020): 1447. http://dx.doi.org/10.3390/s20051447.
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Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.
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Gillies, E. A., A. G. Y. Johnston, and C. R. McInnes. "Action Selection Algorithms for Autonomous Microspacecraft." Journal of Guidance, Control, and Dynamics 22, no. 6 (November 1999): 914–16. http://dx.doi.org/10.2514/2.4473.
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Yuan, Yuyu, Pengqian Zhao, Ting Guo, and Hongpu Jiang. "Counterfactual-Based Action Evaluation Algorithm in Multi-Agent Reinforcement Learning." Applied Sciences 12, no. 7 (March 28, 2022): 3439. http://dx.doi.org/10.3390/app12073439.
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Multi-agent reinforcement learning (MARL) algorithms have made great achievements in various scenarios, but there are still many problems in solving sequential social dilemmas (SSDs). In SSDs, the agent’s actions not only change the instantaneous state of the environment but also affect the latent state which will, in turn, affect all agents. However, most of the current reinforcement learning algorithms focus on analyzing the value of instantaneous environment state while ignoring the study of the latent state, which is very important for establishing cooperation. Therefore, we propose a novel counterfactual reasoning-based multi-agent reinforcement learning algorithm to evaluate the continuous contribution of agent actions on the latent state. We compute that using simulation reasoning and building an action evaluation network. Then through counterfactual reasoning, we can get a single agent’s influence on the environment. Using this continuous contribution as an intrinsic reward enables the agent to consider the collective, thereby promoting cooperation. We conduct experiments in the SSDs environment, and the results show that the collective reward is increased by at least 25% which demonstrates the excellent performance of our proposed algorithm compared to the state-of-the-art algorithms.
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Kim, Beomjoon, Kyungjae Lee, Sungbin Lim, Leslie Kaelbling, and Tomas Lozano-Perez. "Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 9916–24. http://dx.doi.org/10.1609/aaai.v34i06.6546.
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Many important applications, including robotics, data-center management, and process control, require planning action sequences in domains with continuous state and action spaces and discontinuous objective functions. Monte Carlo tree search (MCTS) is an effective strategy for planning in discrete action spaces. We provide a novel MCTS algorithm (voot) for deterministic environments with continuous action spaces, which, in turn, is based on a novel black-box function-optimization algorithm (voo) to efficiently sample actions. The voo algorithm uses Voronoi partitioning to guide sampling, and is particularly efficient in high-dimensional spaces. The voot algorithm has an instance of voo at each node in the tree. We provide regret bounds for both algorithms and demonstrate their empirical effectiveness in several high-dimensional problems including two difficult robotics planning problems.
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Rai, Ankush, and Jagadeesh Kannan R. "A REVIEW ON MACHINE LEARNING ALGORITHMS ON HUMAN ACTION RECOGNITION." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 406. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19977.
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Human action recognition is a vital field of computer vision research. Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human-computer interfaces. The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons. This study gives an overview of different best in class research papers on human movement recognition. Open datasets intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets. We examine both the approaches produced for basic human actions and those for abnormal action states. These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology. Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed. Next, hierarchical recognition approaches for abnormal action states are introduced and looked at. Statistics based methodologies, syntactic methodologies, and description based methodologies for hierarchical recognition is examined in the paper.
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Avoundjian, Tigran, Julia C. Dombrowski, Matthew R. Golden, James P. Hughes, Brandon L. Guthrie, Janet Baseman, and Mauricio Sadinle. "Comparing Methods for Record Linkage for Public Health Action: Matching Algorithm Validation Study." JMIR Public Health and Surveillance 6, no. 2 (April 30, 2020): e15917. http://dx.doi.org/10.2196/15917.
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Background Many public health departments use record linkage between surveillance data and external data sources to inform public health interventions. However, little guidance is available to inform these activities, and many health departments rely on deterministic algorithms that may miss many true matches. In the context of public health action, these missed matches lead to missed opportunities to deliver interventions and may exacerbate existing health inequities. Objective This study aimed to compare the performance of record linkage algorithms commonly used in public health practice. Methods We compared five deterministic (exact, Stenger, Ocampo 1, Ocampo 2, and Bosh) and two probabilistic record linkage algorithms (fastLink and beta record linkage [BRL]) using simulations and a real-world scenario. We simulated pairs of datasets with varying numbers of errors per record and the number of matching records between the two datasets (ie, overlap). We matched the datasets using each algorithm and calculated their recall (ie, sensitivity, the proportion of true matches identified by the algorithm) and precision (ie, positive predictive value, the proportion of matches identified by the algorithm that were true matches). We estimated the average computation time by performing a match with each algorithm 20 times while varying the size of the datasets being matched. In a real-world scenario, HIV and sexually transmitted disease surveillance data from King County, Washington, were matched to identify people living with HIV who had a syphilis diagnosis in 2017. We calculated the recall and precision of each algorithm compared with a composite standard based on the agreement in matching decisions across all the algorithms and manual review. Results In simulations, BRL and fastLink maintained a high recall at nearly all data quality levels, while being comparable with deterministic algorithms in terms of precision. Deterministic algorithms typically failed to identify matches in scenarios with low data quality. All the deterministic algorithms had a shorter average computation time than the probabilistic algorithms. BRL had the slowest overall computation time (14 min when both datasets contained 2000 records). In the real-world scenario, BRL had the lowest trade-off between recall (309/309, 100.0%) and precision (309/312, 99.0%). Conclusions Probabilistic record linkage algorithms maximize the number of true matches identified, reducing gaps in the coverage of interventions and maximizing the reach of public health action.
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Bequette, B. Wayne. "Glucose Clamp Algorithms and Insulin Time-Action Profiles." Journal of Diabetes Science and Technology 3, no. 5 (September 2009): 1005–13. http://dx.doi.org/10.1177/193229680900300503.
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Motivation: Most current insulin pumps include an insulin-on-board (IOB) feature to help subjects avoid problems associated with “insulin stacking.” In addition, many control algorithms proposed for a closed-loop artificial pancreas make use of IOB to reduce the probability of hypoglycemic events that often occur due to the integral action of the controller. The IOB curves are generated from the pharmacodynamic (time-activity profiles) actions of subcutaneous insulin, which are obtained from glycemic clamp studies. Methods: Glycemic clamp algorithms are reviewed and in silico studies are performed to analyze the effect of glucose meter bias and noise on glycemic control and the manipulated glucose infusion rates. The glucose infusion rates are used to obtain insulin time-activity profiles, which are then used to generate IOB curves. Results: A model-based, three-step-ahead controller is shown to be equivalent to a proportional-integral control algorithm with time-delay compensation. A systematic glucose meter bias of +6 mg/dl results in a decrease in the glucose area under the curve of 3% but no change in the IOB profiles. Conclusions: Based on these preliminary simulation studies, a substantial amount of glucose meter bias and noise during a glycemic clamp can be tolerated with little net effect on the IOB curves. It is suggested that handheld glucose meters can therefore be used in clamp studies if the measurements are filtered (averaged) before processing by the control algorithm. Clinical studies are needed to confirm these preliminary results.
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Yamauchi, Sho, and Keiji Suzuki. "Algorithm for Base Action Set Generation Focusing on Undiscovered Sensor Values." Applied Sciences 9, no. 1 (January 4, 2019): 161. http://dx.doi.org/10.3390/app9010161.
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Previous machine learning algorithms use a given base action set designed by hand or enable locomotion for a complicated task through trial and error processes with a sophisticated reward function. These generated actions are designed for a specific task, which makes it difficult to apply them to other tasks. This paper proposes an algorithm to obtain a base action set that does not depend on specific tasks and that is usable universally. The proposed algorithm enables as much interoperability among multiple tasks and machine learning methods as possible. A base action set that effectively changes the external environment was chosen as a candidate. The algorithm obtains this base action set on the basis of the hypothesis that an action to effectively change the external environment can be found by observing events to find undiscovered sensor values. The process of obtaining a base action set was validated through a simulation experiment with a differential wheeled robot.
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WIESE, U. J. "CLUSTER ALGORITHM SOLUTION OF SIGN AND COMPLEX ACTION PROBLEMS." International Journal of Modern Physics B 17, no. 28 (November 10, 2003): 5435–47. http://dx.doi.org/10.1142/s0217979203020545.
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Numerical simulations of numerous quantum systems suffer from notorious sign or complex action problems. In such cases, the Boltzmann factors contributing to the path integral are in general not positive. As a consequence, standard Monte Carlo algorithms based on importance sampling fail. Meron-cluster algorithms realize a general strategy for solving sign problems by canceling explicitly all negative contributions. The remaining uncancelled positive contributions are then generated using importance sampling. The general nature of the sign problem is discussed and its solution with a meron-cluster algorithm is illustrated for staggered lattice fermions that undergo a chiral phase transition. A similar cluster algorithm is used to solve the complex action problem that arises in the Potts model approximation to dense Quantum Chromodynamics (QCD) with heavy quarks.
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Lee, Jongmin, Wonseok Jeon, Geon-Hyeong Kim, and Kee-Eung Kim. "Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4561–68. http://dx.doi.org/10.1609/aaai.v34i04.5885.
Повний текст джерелаАнотація:
Monte-Carlo Tree Search (MCTS) is the state-of-the-art online planning algorithm for large problems with discrete action spaces. However, many real-world problems involve continuous action spaces, where MCTS is not as effective as in discrete action spaces. This is mainly due to common practices such as coarse discretization of the entire action space and failure to exploit local smoothness. In this paper, we introduce Value-Gradient UCT (VG-UCT), which combines traditional MCTS with gradient-based optimization of action particles. VG-UCT simultaneously performs a global search via UCT with respect to the finitely sampled set of actions and performs a local improvement via action value gradients. In the experiments, we demonstrate that our approach outperforms existing MCTS methods and other strong baseline algorithms for continuous action spaces.
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Sharp, Graham R. "Algorithmic Recognition of Group Actions on Orbitals." LMS Journal of Computation and Mathematics 2 (1999): 1–27. http://dx.doi.org/10.1112/s146115700000005x.
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AbstractAn algorithm is given tjat recognises (in O(lN2 log N) time, where N is the size of the input and l the depth of a precalculated Schreier tree) when a transitive group, (G, Ω) is the action on one orbit of the action of G on the set Γ(2) of ordered pairs of distinct elements of some G-set Γ (that us, Ωis isomorphic to an orbital of (G,Γ)). This may be adapted to list all essentially different such actions in O(lN4log N)time, where N is the sum of sizes of the input and output. This will be a useful tool for reducing the degree of a permutation group as an aid to further study of the group.This algorithm is then extended to provide an algorithm that will (in O(lN3 log N) time) recognise when a transiteve group is the action on one orbit of the action of G on the set Γ{2} ofunorderd pairs of distinct elements of some G-set Γ. An algorithm for finding all essentially different such actions is also provided, running in O(lN4logN) time. (again, N is the sum of the input and output sizes.) It is also indicated how these results may be applied to the more general problem of recognising when an intransitive group (G,Ω) is isomorphic to (G, Γ{2}) for some G-set Γ.All the algorithms are practical; most have been implementd in GAP, and the code is made available with this paper. In some cases the algorithms are considerably more practical than their asymptotic analyses would suggest.
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Niazi, Abdolkarim, Norizah Redzuan, Raja Ishak Raja Hamzah, and Sara Esfandiari. "Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making." Advanced Materials Research 566 (September 2012): 572–79. http://dx.doi.org/10.4028/www.scientific.net/amr.566.572.
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In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.
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Liu, Qingqing, Caixia Cui, and Qinqin Fan. "Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method." Mathematics 10, no. 5 (March 3, 2022): 813. http://dx.doi.org/10.3390/math10050813.
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The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.
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Mausam and D. S. Weld. "Planning with Durative Actions in Stochastic Domains." Journal of Artificial Intelligence Research 31 (January 19, 2008): 33–82. http://dx.doi.org/10.1613/jair.2269.
Повний текст джерелаАнотація:
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative actions. This poses severe restrictions in modeling and solving a real world planning problem. We extend the MDP model to incorporate 1) simultaneous action execution, 2) durative actions, and 3) stochastic durations. We develop several algorithms to combat the computational explosion introduced by these features. The key theoretical ideas used in building these algorithms are -- modeling a complex problem as an MDP in extended state/action space, pruning of irrelevant actions, sampling of relevant actions, using informed heuristics to guide the search, hybridizing different planners to achieve benefits of both, approximating the problem and replanning. Our empirical evaluation illuminates the different merits in using various algorithms, viz., optimality, empirical closeness to optimality, theoretical error bounds, and speed.
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Lv, Chun Ying, Ji Yang Wang, and Fei Yu. "Dynamic Spectrum Allocation Using Q-Learning in Cognitive Radio Systems." Applied Mechanics and Materials 427-429 (September 2013): 1579–84. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1579.
Повний текст джерелаАнотація:
In this paper we present an improved dynamic spectrum allocation algorithm based on the intelligence of Q-learning. The state space, action space and reward function of the algorithm are built, and, the agents are guided to perform actions through designing the reward function. Numerical simulation results show that the proposed algorithm can improve system throughput efficiently compared to other algorithms. Facing the status of spectrum resources is tension and spectrum utilization is low, it can also boost the spectrum using condition in the future.
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Sarhan, Shahenda, Mohamed Abu ElSoud, and Hebatullah Rashed. "Enhancing Video Games Policy Based on Least-Squares Continuous Action Policy Iteration: Case Study on StarCraft Brood War and Glest RTS Games and the 8 Queens Board Game." International Journal of Computer Games Technology 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/7090757.
Повний текст джерелаАнотація:
With the rapid advent of video games recently and the increasing numbers of players and gamers, only a tough game with high policy, actions, and tactics survives. How the game responds to opponent actions is the key issue of popular games. Many algorithms were proposed to solve this problem such as Least-Squares Policy Iteration (LSPI) and State-Action-Reward-State-Action (SARSA) but they mainly depend on discrete actions, while agents in such a setting have to learn from the consequences of their continuous actions, in order to maximize the total reward over time. So in this paper we proposed a new algorithm based on LSPI called Least-Squares Continuous Action Policy Iteration (LSCAPI). The LSCAPI was implemented and tested on three different games: one board game, the 8 Queens, and two real-time strategy (RTS) games, StarCraft Brood War and Glest. The LSCAPI evaluation proved superiority over LSPI in time, policy learning ability, and effectiveness.
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Rotberg, Robert I. "The Algorithms of Fame." Journal of Interdisciplinary History 45, no. 2 (August 2014): 209–17. http://dx.doi.org/10.1162/jinh_a_00686.
Повний текст джерелаАнотація:
What exactly is fame? Is it synonymous with renown, with historical importance, or with historical significance? Do importance and significance have the same meaning in this context? Are the most famous women and men—the “big names”—of past times the persons who contributed the most to propelling or stemming the tides of history? Does being merely remembered matter more or less than accomplishing something that made a difference to the pace of human progress? What is the value of quantifying recognition versus qualifying the value added of a particular individual action or bundle of actions? All of these questions, and more, arise from Skiena and Ward’s novel, bold attempt to compare thousands of human endeavors across time and geography in Who’s Bigger? Where Historical Figures Really Rank. It works, in a sense, but does it improve understanding?
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Wang, Wei. "Using Machine Learning Algorithms to Recognize Shuttlecock Movements." Wireless Communications and Mobile Computing 2021 (June 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/9976306.
Повний текст джерелаАнотація:
Shuttlecock is an excellent traditional national sport in China. Because of its simplicity, convenience, and fun, it is loved by the broad masses of people, especially teenagers and children. The development of shuttlecock sports into a confrontational event is not long, and it takes a period of research to master the tactics and strategies of shuttlecock sports. Based on this, this article proposes the use of machine learning algorithms to recognize the movement of shuttlecock movements, aiming to provide more theoretical and technical support for shuttlecock competitions by identifying features through actions with the assistance of technical algorithms. This paper uses literature research methods, model methods, comparative analysis methods, and other methods to deeply study the motion characteristics of shuttlecock motion, the key algorithms of machine learning algorithms, and other theories and construct the shuttlecock motion recognition based on multiview clustering algorithm. The model analyzes the robustness and accuracy of the machine learning algorithm and other algorithms, such as a variety of performance comparisons, and the results of the shuttlecock motion recognition image. For the key movements of shuttlecock movement, disk, stretch, hook, wipe, knock, and abduction, the algorithm proposed in this paper has a good movement recognition rate, which can reach 91.2%. Although several similar actions can be recognized well, the average recognition accuracy rate can exceed 75%, and even through continuous image capture, the number of occurrences of the action can be automatically analyzed, which is beneficial to athletes. And the coach can better analyze tactics and research strategies.
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Torabi, Faraz. "Imitation Learning from Observation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9900–9901. http://dx.doi.org/10.1609/aaai.v33i01.33019900.
Повний текст джерелаАнотація:
Humans and other animals have a natural ability to learn skills from observation, often simply from seeing the effects of these skills: without direct knowledge of the underlying actions being taken. For example, after observing an actor doing a jumping jack, a child can copy it despite not knowing anything about what's going on inside the actor's brain and nervous system. The main focus of this thesis is extending this ability to artificial autonomous agents, an endeavor recently referred to as "imitation learning from observation." Imitation learning from observation is especially relevant today due to the accessibility of many online videos that can be used as demonstrations for robots. Meanwhile, advances in deep learning have enabled us to solve increasingly complex control tasks mapping visual input to motor commands. This thesis contributes algorithms that learn control policies from state-only demonstration trajectories. Two types of algorithms are considered. The first type begins by recovering the missing action information from demonstrations and then leverages existing imitation learning algorithms on the full state-action trajectories. Our preliminary work has shown that learning an inverse dynamics model of the agent in a self-supervised fashion and then inferring the actions performed by the demonstrator enables sufficient action recovery for this purpose. The second type of algorithm uses model-free end-to-end learning. Our preliminary results indicate that iteratively optimizing a policy based on the closeness of the imitator's and expert's state transitions leads to a policy that closely mimics the demonstrator's trajectories.
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Suzuki, Takayuki, Yu Wang, Jien Kato, and Kenji Mase. "Classifier Learning Algorithms for Cross-Dataset Action Recognition." IEEJ Transactions on Electronics, Information and Systems 135, no. 12 (2015): 1574–82. http://dx.doi.org/10.1541/ieejeiss.135.1574.
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Albers, Susanne, Moses Charikar, and Michael Mitzenmacher. "Delayed Information and Action in On-Line Algorithms." Information and Computation 170, no. 2 (November 2001): 135–52. http://dx.doi.org/10.1006/inco.2001.3057.
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Ralston, Anthony. "Microcomputer Algorithms, Action from Algebra (John P. Killingbeck)." SIAM Review 35, no. 3 (September 1993): 533–34. http://dx.doi.org/10.1137/1035126.
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Barriales-Villa, Roberto, Juan Ramón Gimeno-Blanes, Esther Zorio-Grima, Tomás Ripoll-Vera, Artur Evangelista-Masip, Angel Moya-Mitjans, Luis Serratosa-Fernández, Dimpna C. Albert-Brotons, José Manuel García-Pinilla, and Pablo García-Pavía. "Plan of Action for Inherited Cardiovascular Diseases: Synthesis of Recommendations and Action Algorithms." Revista Española de Cardiología (English Edition) 69, no. 3 (March 2016): 300–309. http://dx.doi.org/10.1016/j.rec.2015.11.029.
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Liu, Quan, Xiang Mu, Wei Huang, Qiming Fu та Yonggang Zhang. "A Sarsa(λ) Algorithm Based on Double-Layer Fuzzy Reasoning". Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/561026.
Повний текст джерелаАнотація:
Solving reinforcement learning problems in continuous space with function approximation is currently a research hotspot of machine learning. When dealing with the continuous space problems, the classicQ-iteration algorithms based on lookup table or function approximation converge slowly and are difficult to derive a continuous policy. To overcome the above weaknesses, we propose an algorithm named DFR-Sarsa(λ) based on double-layer fuzzy reasoning and prove its convergence. In this algorithm, the first reasoning layer uses fuzzy sets of state to compute continuous actions; the second reasoning layer uses fuzzy sets of action to compute the components ofQ-value. Then, these two fuzzy layers are combined to compute theQ-value function of continuous action space. Besides, this algorithm utilizes the membership degrees of activation rules in the two fuzzy reasoning layers to update the eligibility traces. Applying DFR-Sarsa(λ) to the Mountain Car and Cart-pole Balancing problems, experimental results show that the algorithm not only can be used to get a continuous action policy, but also has a better convergence performance.
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Gao, Yan Song, and Yong Sha. "Preliminary Analysis on Wall Beam Force Algorithms under the Action of Vertical Load." Applied Mechanics and Materials 166-169 (May 2012): 2841–46. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.2841.
Повний текст джерелаАнотація:
This paper makes a study on the stress state and internal force algorithms of wall beams under the action of vertical load. It also makes a comparative analysis to several typical wall-beam designs at present. Then, by using the finite element, it verifies the accuracy and reliability of internal force algorithm formulas of wall beams in current Code for Design of Masonry Structure, hoping to provide a solid theoretical reference for the selection of wall beam algorithm designs.
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Ye, Weicheng, and Dangxing Chen. "Analysis of Performance Measure in Q Learning with UCB Exploration." Mathematics 10, no. 4 (February 12, 2022): 575. http://dx.doi.org/10.3390/math10040575.
Повний текст джерелаАнотація:
Compared to model-based Reinforcement Learning (RL) approaches, model-free RL algorithms, such as Q-learning, require less space and are more expressive, since specifying value functions or policies is more flexible than specifying the model for the environment. This makes model-free algorithms more prevalent in modern deep RL. However, model-based methods can more efficiently extract the information from available data. The Upper Confidence Bound (UCB) bandit can improve the exploration bonuses, and hence increase the data efficiency in the Q-learning framework. The cumulative regret of the Q-learning algorithm with an UCB exploration policy in the episodic Markov Decision Process has recently been explored in the underlying environment of finite state-action space. In this paper, we study the regret bound of the Q-learning algorithm with UCB exploration in the scenario of compact state-action metric space. We present an algorithm that adaptively discretizes the continuous state-action space and iteratively updates Q-values. The algorithm is able to efficiently optimize rewards and minimize cumulative regret.
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Yamada, Kazuaki. "Expression of Continuous State and Action Spaces forQ-Learning Using Neural Networks and CMAC." Journal of Robotics and Mechatronics 24, no. 2 (April 20, 2012): 330–39. http://dx.doi.org/10.20965/jrm.2012.p0330.
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This paper proposes a new reinforcement learning algorithm that can learn, using neural networks and CMAC, a mapping function between highdimensional sensors and the motors of an autonomous robot. Conventional reinforcement learning algorithms require a lot of memory because they use lookup tables to describe high-dimensional mapping functions. Researchers have therefore tried to develop reinforcement learning algorithms that can learn the high-dimensional mapping functions. We apply the proposed method to an autonomous robot navigation problem and a multi-link robot arm reaching problem, and we evaluate the effectiveness of the method.
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Li, Yueru. "Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network." Complexity 2021 (June 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/2656275.
Повний текст джерелаАнотація:
In this paper, through an in-depth study and analysis of dance motion capture algorithms in wearable sensor networks, the extended Kalman filter algorithm and the quaternion method are selected after analysing a variety of commonly used data fusion algorithms and pose solving algorithms. In this paper, a sensor-body coordinate system calibration algorithm based on hand-eye calibration is proposed, which only requires three calibration poses to complete the calibration of the whole-body sensor-body coordinate system. In this paper, joint parameter estimation algorithm based on human joint constraints and limb length estimation algorithm based on closed joint chains are proposed, respectively. The algorithm is an iterative optimization algorithm that divides each iteration into an expectation step and a great likelihood step, and the best convergence value can be found efficiently according to each iteration step. The feature values of each pose action are fed into the algorithm for model learning, which enables the training of the model. The trained model is then tested by combining the collected gesture data with the algorithmic model to recognize and classify the gesture data, observe its recognition accuracy, and continuously optimize the model to achieve accurate recognition of human gesture actions.
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Uc-Cetina, Víctor. "A Novel Reinforcement Learning Architecture for Continuous State and Action Spaces." Advances in Artificial Intelligence 2013 (April 18, 2013): 1–10. http://dx.doi.org/10.1155/2013/492852.
Повний текст джерелаАнотація:
We introduce a reinforcement learning architecture designed for problems with an infinite number of states, where each state can be seen as a vector of real numbers and with a finite number of actions, where each action requires a vector of real numbers as parameters. The main objective of this architecture is to distribute in two actors the work required to learn the final policy. One actor decides what action must be performed; meanwhile, a second actor determines the right parameters for the selected action. We tested our architecture and one algorithm based on it solving the robot dribbling problem, a challenging robot control problem taken from the RoboCup competitions. Our experimental work with three different function approximators provides enough evidence to prove that the proposed architecture can be used to implement fast, robust, and reliable reinforcement learning algorithms.
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S., Pradeep, and Jagadish S. Kallimani. "Machine Learning Based Predictive Action on Categorical Non-Sequential Data." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 1020–30. http://dx.doi.org/10.2174/2213275912666190417150421.
Повний текст джерелаАнотація:
Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.
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V.V. Kumar, K., and P. V.V. Kishore. "Indian classical dance action identification using adaptive graph matching from unconstrained videos." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 500. http://dx.doi.org/10.14419/ijet.v7i1.1.10156.
Повний текст джерелаАнотація:
Extracting and recognizing complex human movements from unconstraint online video sequence is a challenging task. In this work the problem becomes complicated by the use of unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern features for segmentation. We also explore multiple feature fusion models with early fusion and late fusion techniques for improving the classification process. The extracted features were represented as a graph and a novel adaptive graph matching algorithm is proposed. We test the algorithms on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.
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Chandak, Yash, Georgios Theocharous, Blossom Metevier, and Philip Thomas. "Reinforcement Learning When All Actions Are Not Always Available." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3381–88. http://dx.doi.org/10.1609/aaai.v34i04.5740.
Повний текст джерелаАнотація:
The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available decisions (actions) at each time step is stochastic. Recently, the stochastic action set Markov decision process (SAS-MDP) formulation has been proposed, which better captures the concept of a stochastic action set. In this paper we argue that existing RL algorithms for SAS-MDPs can suffer from potential divergence issues, and present new policy gradient algorithms for SAS-MDPs that incorporate variance reduction techniques unique to this setting, and provide conditions for their convergence. We conclude with experiments that demonstrate the practicality of our approaches on tasks inspired by real-life use cases wherein the action set is stochastic.
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Zhong, Xuejiao. "Action Recognition, Tracking, and Optimization Analysis of Training Process Based on SVR Model and Multimedia Technology." Advances in Multimedia 2022 (April 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/8641958.
Повний текст джерелаАнотація:
In order to explore the action recognition, tracking, and optimization analysis of the training process based on the SVR model and multimedia technology, the author proposes based on the radial basis function model, researching a new surrogate model technology-support vector regression (SVR). We first introduce the basic principles of SVR, select the parameters of SVR, and then elaborate the basic steps of SVR modeling. Then, we design and optimize application examples through numerical example multimedia technology; the validity of the support vector regression method is verified. Experimental results: the comparison of SVR1 and SVR2 shows that the utilization of multiscale timing feature maps should occur after tem (SVR2) rather than being directly fused in the feature dimension (SVR1), mainly because small-scale information affects the resolution of large-scale information; on data sets such as ActivityNet, in order to verify the effectiveness of SVR and DR-Dvc algorithms, the performance of the proposed algorithm and the baseline before improvement and the current mainstream algorithm are respectively compared. Experimental results show the proposed algorithm has a significant performance improvement compared to before the improvement; at the same time, it is better than most current mainstream algorithms, which proves the feasibility and effectiveness of the algorithm. Describing the introduction of regression can effectively improve the performance of sequential action proposals and event description algorithms, and compared with the current mainstream methods, it has certain performance advantages.
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Mariño, Julian R. H., Rubens O. Moraes, Claudio Toledo, and Levi H. S. Lelis. "Evolving Action Abstractions for Real-Time Planning in Extensive-Form Games." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2330–37. http://dx.doi.org/10.1609/aaai.v33i01.33012330.
Повний текст джерелаАнотація:
A key challenge for planning systems in real-time multiagent domains is to search in large action spaces to decide an agent’s next action. Previous works showed that handcrafted action abstractions allow planning systems to focus their search on a subset of promising actions. In this paper we show that the problem of generating action abstractions can be cast as a problem of selecting a subset of pure strategies from a pool of options. We model the selection of a subset of pure strategies as a two-player game in which the strategy set of the players is the powerset of the pool of options— we call this game the subset selection game. We then present an evolutionary algorithm for solving such a game. Empirical results on small matches of µRTS show that our evolutionary approach is able to converge to a Nash equilibrium for the subset selection game. Also, results on larger matches show that search algorithms using action abstractions derived by our evolutionary approach are able to substantially outperform all state-of-the-art planning systems tested.
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van Lier, Ben. "From High Frequency Trading to Self-Organizing Moral Machines." International Journal of Technoethics 7, no. 1 (January 2016): 34–50. http://dx.doi.org/10.4018/ijt.2016010103.
Повний текст джерелаАнотація:
Technology is responsible for major systemic changes within the global financial sector in general and particularly in the trade in financial products. The global financial sector has already developed into a comprehensive network of mutually connected people and computers that are constantly evaluating and approving millions of transactions. Algorithms play a crucial role within this global financial network. An algorithm is in essence merely a set of instructions developed by one or more people with the intention of having these instructions performed by a machine such as a computer, a software robot or a physical robot in order to realize an ideal result. As part of a development in which we as human beings have ever higher expectations of algorithms and these algorithms become ever more autonomous in their actions, we cannot avoid including possibilities in these algorithms that enable ethical or more considerations. To develop this ethical or moral consideration, we need a kind of ethical framework which can be used for developing algorithms. With the development of such a framework we can start to think about what we as human beings consider to be moral action by machines within the financial sector based on such a framework.
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Maldonato, Mauro, and Silvia Dell’ Orco. "Decision Making Styles and Adaptive Algorithms for Human Action." Psychology 02, no. 08 (2011): 811–16. http://dx.doi.org/10.4236/psych.2011.28124.
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Si, Haifei, Xingliu Hu, and Yizhi Wang. "Research on Video-Based Human Action Behavior Recognition Algorithms." IOP Conference Series: Earth and Environmental Science 440 (March 19, 2020): 032142. http://dx.doi.org/10.1088/1755-1315/440/3/032142.
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Gaber, Jaafar. "Action Selection Algorithms for Autonomous System in Pervasive Environment." ACM Transactions on Autonomous and Adaptive Systems 6, no. 1 (February 2011): 1–6. http://dx.doi.org/10.1145/1921641.1921651.
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Syed, Z., E. Vigmond, S. Nattel, and L. J. Leon. "Atrial cell action potential parameter fitting using genetic algorithms." Medical & Biological Engineering & Computing 43, no. 5 (October 2005): 561–71. http://dx.doi.org/10.1007/bf02351029.
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Vidhyapathi, C. M., Alex Noel Joseph Raj, and S. Sundar. "The 3D-DTW Custom IP based FPGA Hardware Acceleration for Action Recognition." Journal of Imaging Science and Technology 65, no. 1 (January 1, 2021): 10401–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2021.65.1.010401.
Повний текст джерелаАнотація:
Abstract This article proposes an implementation of an action recognition system, which allows the user to perform operations in real time. The Microsoft Kinect (RGB-D) sensor plays a central role in this system, which provides the skeletal joint information of humans directly. Computationally efficient skeletal joint position features are considered for describing each action. The dynamic time warping algorithm (DTW) is a widely used algorithm in many applications such as similarity sequence search, classification, and speech recognition. It provides the highest accuracy compared to all other algorithms. However, the computational time of the DTW algorithm is a major drawback in real world applications. To speed up the basic DTW algorithm, a novel three-dimensional dynamic time warping (3D-DTW) classification algorithm is proposed in this work. The proposed 3D-DTW algorithm is implemented in both software and field programmable gate array (FPGA) hardware modeling techniques. The performance of the 3D-DTW algorithm is evaluated for 12 actions in which each action is described with the feature vector size of 576 over 32 frames. From our software modeling results, it has been shown that the proposed algorithm performs the action classification accurately. However, the computation time of the 3D-DTW algorithm increases linearly when we increase either the number of actions or the feature vector size of each action. For further speedup, an efficient custom 3D-DTW intellectual property (IP) core is developed using the Xilinx Vivado high-level synthesis (HLS) tool to accelerate the 3D-DTW algorithm in FPGA hardware. The CPU centric software modeling of the 3D-DTW algorithm is compared with its hardware accelerated custom IP core. It has been shown that the developed 3D-DTW Custom IP core computation time is 40 times faster than its software counterpart. As the hardware results are promising, a parallel hardware software co-design architecture is proposed for the Xilinx Zynq-7020 System on Chip (SoC) FPGA for action recognition. The HLS simulation and synthesis results are provided to support the practical implementation of the proposed architecture. Our proposed approach outperforms many of the existing state-of-the-art DTW based action recognition techniques by providing the highest accuracy of 97.77%.