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Gotowe bibliografie tematyczne / Single target tracking algorithms

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Gotowa bibliografia na temat „Single target tracking algorithms”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Single target tracking algorithms"

1

Ling, Jiankun. "Target Tracking Using Kalman Filter Based Algorithms." Journal of Physics: Conference Series 2078, no. 1 (2021): 012020. http://dx.doi.org/10.1088/1742-6596/2078/1/012020.

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Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simul

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2

ZhongMing Liao and Azlan Ismail. "Performance of Correlational Filtering and Deep Learning Based Single Target Tracking Algorithms." Journal of Smart Science and Technology 3, no. 1 (2023): 63–79. http://dx.doi.org/10.24191/jsst.v3i1.42.

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Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream

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3

Qu, Zhiyi, Xue Zhao, Huihui Xu, Hongying Tang, Jiang Wang, and Baoqing Li. "An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking." Sensors 22, no. 18 (2022): 6972. http://dx.doi.org/10.3390/s22186972.

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Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this pa

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4

Zhang, Haozheng, Xiong Li, and Yu Meng. "Performance Study of Two Bearings-only Target Tracking Algorithms." Journal of Physics: Conference Series 2419, no. 1 (2023): 012086. http://dx.doi.org/10.1088/1742-6596/2419/1/012086.

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Abstract For the bearings-only target tracking for single array, the tracking performance of extended kalman filter algorithm in cartesian coordinates and modified polar coordinates is studied. The result shows that the performance of extended kalman filter algorithm in polar coordinates is more general than that in cartesian coordinates. In addition, the tracking performance of these two algorithms decreases with an increase in azimuth measurement error.

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5

Yuan, Xianghui, Feng Lian, and Chongzhao Han. "Models and Algorithms for Tracking Target with Coordinated Turn Motion." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649276.

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Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Fi

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6

Wei, Hao, Zong-ping Cai, Bin Tang, and Ze-xiang Yu. "Review of the algorithms for radar single target tracking." IOP Conference Series: Earth and Environmental Science 69 (June 2017): 012073. http://dx.doi.org/10.1088/1755-1315/69/1/012073.

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7

Zhang, Ming, Li Wang, Hai Hua Shi, and Wei Xiang. "The Target Tracking Algorithm Research of Independent Vision Robot Fish." Advanced Materials Research 753-755 (August 2013): 2015–19. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2015.

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In the independent vision robot fish games, the interference of water wave often causes tracking inaccuracy and target tracking failure. In order to solve these problems, the Meanshift algorithm and the combination of Meanshift algorithm and Kalman filter respectively are studied to realize target tracking of independent vision robot fish in this paper. By comparing the two algorithms, the results show that: the former tracking algorithm is not ideal and easy to lose the target. The combined algorithm of Meanshift and Kalman filter can effectively improve the performance of single-target track

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8

Chang-Jian Wang, Chang-Jian Wang, Yong Ding Chang-Jian Wang, and Ye Ji Yong Ding. "An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD." 電腦學刊 33, no. 2 (2022): 069–81. http://dx.doi.org/10.53106/199115992022043302006.

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<p>This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale se

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9

Chang-Jian Wang, Chang-Jian Wang, Yong Ding Chang-Jian Wang, and Ye Ji Yong Ding. "An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD." 電腦學刊 33, no. 2 (2022): 069–81. http://dx.doi.org/10.53106/199115992022043302006.

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<p>This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale se

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10

Guo, Xifeng, Turdi Tohti, Mayire Ibrayim, and Askar Hamdulla. "Research on Anti-Occlusion Correlation Filtering Tracking Algorithm Based on Adaptive Scale." Information 13, no. 3 (2022): 131. http://dx.doi.org/10.3390/info13030131.

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Target tracking has always been an important research direction in the field of computer vision. The target tracking method based on correlation filtering has become a research hotspot in the field of target tracking due to its efficiency and robustness. In recent years, a series of new developments have been made in this research. However, traditional correlation filtering algorithms cannot achieve real-time tracking in complex scenes such as illumination changes, target occlusion, motion deformation, and motion blur due to their single characteristics and insufficient background information.

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