Journal articles: 'Distributed algorithms for camera networks'

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Anjum, Nadeem. "Camera Localization in Distributed Networks Using Trajectory Estimation." Journal of Electrical and Computer Engineering 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/604647.

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This paper presents an algorithm for camera localization using trajectory estimation (CLUTE) in a distributed network of nonoverlapping cameras. The algorithm recovers the extrinsic calibration parameters, namely, the relative position and orientation of the camera network on a common ground plane coordinate system. We first model the observed trajectories in each camera's field of view using Kalman filtering, then we use this information to estimate the missing trajectory information in the unobserved areas by fusing the results of a forward and backward linear regression estimation from adjacent cameras. These estimated trajectories are then filtered and used to recover the relative position and orientation of the cameras by analyzing the estimated and observedexitandentrypoints of an object in each camera's field of view. The final configuration of the network is established by considering one camera as a reference and by adjusting the remaining cameras with respect to this reference. We demonstrate the algorithm on both simulated and real data and compare the results with state-of-the-art approaches. The experimental results show that the proposed algorithm is more robust to noisy and missing data and in case of camera failure.

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Zhao, Long, Zhen Liu, Tiejun Li, Baoqi Huang, and Lihua Xie. "Moving Target Positioning Based on a Distributed Camera Network." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/803743.

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We propose a systematic framework for moving target positioning based on a distributed camera network. In the proposed framework, low-cost static cameras are deployed to cover a large region, moving targets are detected and then tracked using corresponding algorithms, target positions are estimated by making use of the geometrical relationships among those cameras after calibrating those cameras, and finally, for each target, its position estimates obtained from different cameras are unified into the world coordinate system. This system can function as complementary positioning information sources to realize moving target positioning in indoor or outdoor environments when global navigation satellite system (GNSS) signals are unavailable. The experiments are carried out using practical indoor and outdoor environment data, and the experimental results show that the systematic framework and inclusive algorithms are both effective and efficient.

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Wu, Yi-Chang, Ching-Han Chen, Yao-Te Chiu, and Pi-Wei Chen. "Cooperative People Tracking by Distributed Cameras Network." Electronics 10, no. 15 (July 25, 2021): 1780. http://dx.doi.org/10.3390/electronics10151780.

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In the application of video surveillance, reliable people detection and tracking are always challenging tasks. The conventional single-camera surveillance system may encounter difficulties such as narrow-angle of view and dead space. In this paper, we proposed multi-cameras network architecture with an inter-camera hand-off protocol for cooperative people tracking. We use the YOLO model to detect multiple people in the video scene and incorporate the particle swarm optimization algorithm to track the person movement. When a person leaves the area covered by a camera and enters an area covered by another camera, these cameras can exchange relevant information for uninterrupted tracking. The motion smoothness (MS) metrics is proposed for evaluating the tracking quality of multi-camera networking system. We used a three-camera system for two persons tracking in overlapping scene for experimental evaluation. Most tracking person offsets at different frames were lower than 30 pixels. Only 0.15% of the frames showed abrupt increases in offsets pixel. The experiment results reveal that our multi-camera system achieves robust, smooth tracking performance.

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Eriksson, Emil, Gyorgy Dan, and Viktoria Fodor. "Coordinating Distributed Algorithms for Feature Extraction Offloading in Multi-Camera Visual Sensor Networks." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 11 (November 2018): 3288–99. http://dx.doi.org/10.1109/tcsvt.2017.2745102.

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Giordano, Jacopo, Margherita Lazzaretto, Giulia Michieletto, and Angelo Cenedese. "Visual Sensor Networks for Indoor Real-Time Surveillance and Tracking of Multiple Targets." Sensors 22, no. 7 (March 30, 2022): 2661. http://dx.doi.org/10.3390/s22072661.

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The recent trend toward the development of IoT architectures has entailed the transformation of the standard camera networks into smart multi-device systems capable of acquiring, elaborating, and exchanging data and, often, dynamically adapting to the environment. Along this line, this work proposes a novel distributed solution that guarantees the real-time monitoring of 3D indoor structured areas and also the tracking of multiple targets, by employing a heterogeneous visual sensor network composed of both fixed and Pan-Tilt-Zoom (PTZ) cameras. The fulfillment of the twofold mentioned goal was ensured through the implementation of a distributed game-theory-based algorithm, aiming at optimizing the controllable parameters of the PTZ devices. The proposed solution is able to deal with the possible conflicting requirements of high tracking precision and maximum coverage of the surveilled area. Extensive numerical simulations in realistic scenarios validated the effectiveness of the outlined strategy.

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Chebi, Hocine, Abdelkader Benaissa, and Rafik Sayah. "Contribution to the Maximum Coverage Detection in a Heterogeneous Network." International Journal of Applied Evolutionary Computation 11, no. 3 (July 2020): 1–19. http://dx.doi.org/10.4018/ijaec.2020070101.

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This article has addressed the problem of area coverage in surveillance camera networks using a minimum number of active sensors. The dense and random deployment of cameras creates many problems, among which the same portion of the area of interest is cited and monitored by several sensors. This redundancy of information generates unnecessary energy consumption, which increases the cost of installation. This work contributed to the extension of a surveillance algorithm, and the authors presented in this work a distributed algorithm of perimeter surveillance and made this contribution allowing the maintenance of total coverage in heterogeneous camera networks. The proposed solution is based on the search for minimum sets that completely cover a surface by scheduling the activity of the sensors. The proposed approach consists of calculating the distance between the center and the furthest point not covered and subtracting a fixed step from it; the coverage of these circles is done in the same way as the coverage of the first perimeter. The results of the simulations show that this approach ensures maximum coverage with a minimum number of cameras.

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Song, Wenzhan, Fangyu Li, Maria Valero, and Liang Zhao. "Toward Creating a Subsurface Camera." Sensors 19, no. 2 (January 14, 2019): 301. http://dx.doi.org/10.3390/s19020301.

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In this article, the framework and architecture of a Subsurface Camera (SAMERA) are envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals and computes a 3D subsurface image in situ in real time. The basic mechanism is geophysical waves propagating/reflected/refracted through subsurface enter a network of geophysical sensors, where a 2D or 3D image is computed and recorded; control software may be connected to this network to allow view of the 2D/3D image and adjustment of settings such as resolution, filter, regularization, and other algorithm parameters. System prototypes based on seismic imaging have been designed. SAMERA technology is envisioned as a game changer to transform many subsurface survey and monitoring applications, including oil/gas exploration and production, subsurface infrastructures and homeland security, wastewater and CO2 sequestration, and earthquake and volcano hazard monitoring. System prototypes for seismic imaging have been built. Creating SAMERA requires interdisciplinary collaboration and the transformation of sensor networks, signal processing, distributed computing, and geophysical imaging.

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Chen, Yanming, Qingjie Zhao, Zhulin An, Peng Lv, and Liujun Zhao. "Distributed Multi-Target Tracking Based on the K-MTSCF Algorithm in Camera Networks." IEEE Sensors Journal 16, no. 13 (July 2016): 5481–90. http://dx.doi.org/10.1109/jsen.2016.2565263.

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Luo, Jiajia, Wei Wang, and Hairong Qi. "Feature Extraction and Representation for Distributed Multi-View Human Action Recognition." Emerging and Selected Topics in Circuits and Systems, IEEE Journal on 3, no. 2 (June 2013): 145–54. http://dx.doi.org/10.1109/jetcas.2013.2256824.

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Multi-view human action recognition has gained a lot of attention in recent years for its superior performance as compared to single view recognition. In this paper, we propose a new framework for the real-time realization of human action recognition in distributed camera networks (DCNs). We first present a new feature descriptor (Mltp-hist) that is tolerant to illumination change, robust in homogeneous region and computationally efficient. Taking advantage of the proposed Mltp-hist, the noninformative 3-D patches generated from the background can be further removed automatically that effectively highlights the foreground patches. Next, a new feature representation method based on sparse coding is presented to generate the histogram representation of local videos to be transmitted to the base station for classification. Due to the sparse representation of extracted features, the approximation error is reduced. Finally, at the base station, a probability model is produced to fuse the information from various views and a class label is assigned accordingly. Compared to the existing algorithms, the proposed framework has three advantages while having less requirements on memory and bandwidth consumption: 1) no preprocessing is required; 2) communication among cameras is unnecessary; and 3) positions and orientations of cameras do not need to be fixed. We further evaluate the proposed framework on the most popular multi-view action dataset IXMAS. Experimental results indicate that our proposed framework repeatedly achieves state-of-the-art results when various numbers of views are tested. In addition, our approach is tolerant to the various combination of views and benefit from introducing more views at the testing stage. Especially, our results are still satisfactory even when large misalignment exists between the training and testing samples.

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Niu, Zhenyu. "Voice Detection and Deep Learning Algorithms Application in Remote English Translation Classroom Monitoring." Mobile Information Systems 2022 (July 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/3340999.

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With the continuous development of cellular networks, the traffic from voice services increases gradually. The wireless sensor network (WSN) is a distributed network consisting of a large number of peripheral nodes distributed in the surveillance area. The nodes in the network complete it in a self-organizing form, and the sink node collects the data from each sensor node. When sending data, the nodes near the receiver will quickly run out of energy and cannot perform further transmission tasks. The resulting “power supply emptiness” problem has a great impact on network performance. Therefore, the power consumption of the network must be considered when designing the WSN routing algorithm. In order to effectively improve students’ academic performance and monitor students’ teaching conditions, the classroom remote monitoring system places two cameras in the university’s English translation classroom and uses technology to merge the information to execute the entire process. By recording the course, we can save the teacher’s classroom content and the student’s classroom performance and upload the recorded video in real time. In addition, the classroom remote monitoring system is a multiclient system, divided into teacher and student terminals. The user can log in, watch the video, and perform other necessary operations.

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An, Feng-Ping, Jun-e. Liu, and Lei Bai. "Pedestrian Reidentification Algorithm Based on Deconvolution Network Feature Extraction-Multilayer Attention Mechanism Convolutional Neural Network." Journal of Sensors 2021 (January 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/9463092.

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Pedestrian reidentification is a key technology in large-scale distributed camera systems. It can quickly and efficiently detect and track target people in large-scale distributed surveillance networks. The existing traditional pedestrian reidentification methods have problems such as low recognition accuracy, low calculation efficiency, and weak adaptive ability. Pedestrian reidentification algorithms based on deep learning have been widely used in the field of pedestrian reidentification due to their strong adaptive ability and high recognition accuracy. However, the pedestrian recognition method based on deep learning has the following problems: first, during the learning process of the deep learning model, the initial value of the convolution kernel is usually randomly assigned, which makes the model learning process easily fall into a local optimum. The second is that the model parameter learning method based on the gradient descent method exhibits gradient dispersion. The third is that the information transfer of pedestrian reidentification sequence images is not considered. In view of these issues, this paper first examines the feature map matrix from the original image through a deconvolution neural network, uses it as a convolution kernel, and then performs layer-by-layer convolution and pooling operations. Then, the second derivative information of the error function is directly obtained without calculating the Hessian matrix, and the momentum coefficient is used to improve the convergence of the backpropagation, thereby suppressing the gradient dispersion phenomenon. At the same time, to solve the problem of information transfer of pedestrian reidentification sequence images, this paper proposes a memory network model based on a multilayer attention mechanism, which uses the network to effectively store image visual information and pedestrian behavior information, respectively. It can solve the problem of information transmission. Based on the above ideas, this paper proposes a pedestrian reidentification algorithm based on deconvolution network feature extraction-multilayer attention mechanism convolutional neural network. Experiments are performed on the related data sets using this algorithm and other major popular human reidentification algorithms. The results show that the pedestrian reidentification method proposed in this paper not only has strong adaptive ability but also has significantly improved average recognition accuracy and rank-1 matching rate compared with other mainstream methods.

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Rocha Neto, Aluizio, Thiago P. Silva, Thais Batista, Flávia C. Delicato, Paulo F. Pires, and Frederico Lopes. "Leveraging Edge Intelligence for Video Analytics in Smart City Applications." Information 12, no. 1 (December 31, 2020): 14. http://dx.doi.org/10.3390/info12010014.

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In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

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Yang, Peihao, Linghe Kong, Meikang Qiu, Xue Liu, and Guihai Chen. "Compressed Imaging Reconstruction with Sparse Random Projection." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1 (April 16, 2021): 1–25. http://dx.doi.org/10.1145/3447431.

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As the Internet of Things thrives, monitors and cameras produce tons of image data every day. To efficiently process these images, many compressed imaging frameworks are proposed. A compressed imaging framework comprises two parts, image signal measurement and reconstruction. Although a plethora of measurement devices have been designed, the development of the reconstruction is relatively lagging behind. Nowadays, most of existing reconstruction algorithms in compressed imaging are optimization problem solvers based on specific priors. The computation burdens of these optimization algorithms are enormous and the solutions are usually local optimums. Meanwhile, it is inconvenient to deploy these algorithms on cloud, which hinders the popularization of compressed imaging. In this article, we dive deep into the random projection to build reconstruction algorithms for compressed imaging. We first fully utilize the information in the measurement procedure and propose a combinatorial sparse random projection (SRP) reconstruction algorithm. Then, we generalize the SRP to a novel distributed algorithm called Cloud-SRP (CSRP), which enables efficient reconstruction on cloud. Moreover, we explore the combination of SRP with conventional optimization reconstruction algorithms and propose the Iterative-SRP (ISRP), which converges to a guaranteed fixed point. With minor modifications on the naive optimization algorithms, the ISRP yields better reconstructions. Experiments on real ghost imaging reconstruction reveal that our algorithms are effective. And simulation experiments show their advantages over the classical algorithms.

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Alsabhan, Manal, Adel Soudani, and Manan Almusallam. "A distributed scheme for energy-efficient event-based target recognition using Internet of Multimedia Things." International Journal of Distributed Sensor Networks 18, no. 5 (May 2022): 155013292211003. http://dx.doi.org/10.1177/15501329221100326.

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The availability of low-cost embedded devices for multimedia sensing has encouraged their integration with low-power wireless sensors to create systems that enable advanced services and applications referred to as the Internet of Multimedia Things. Image-based sensing applications are challenged by energy efficiency and resource availability. Mainly, image sensing and transmission in Internet of Multimedia Things severely deplete the sensor energy and overflow the network bandwidth with redundant data. Some solutions presented in the literature, such as image compression, do not efficiently solve this problem because of the algorithms’ computational complexities. Thus, detecting the event of interest locally before the communication using shape-based descriptors would avoid useless data transmission and would extend the network lifetime. In this article, we propose a new approach of distributed event-based sensing scheme over a set of nodes forming a processing cluster to balance the processing load. This approach is intended to reduce per-node energy consumption in one sensing cycle. The conducted experiments show that our novel method based on the general Fourier descriptor decreases the energy consumption in the camera node to only 2.4 mJ, which corresponds to 75.32% of energy-saving compared to the centralized approach, promising to prolong the network lifetime significantly. In addition, the scheme achieved more than 95% accuracy in target recognition.

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Chen, Yanming, and Qingjie Zhao. "A Novel Square-Root Cubature Information Weighted Consensus Filter Algorithm for Multi-Target Tracking in Distributed Camera Networks." Sensors 15, no. 5 (May 5, 2015): 10526–46. http://dx.doi.org/10.3390/s150510526.

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Salim, Ahmed, and Hagar Ramdan. "Clustering Algorithm Based on the Direction of Overlapping Field of Views for Wireless Multimedia Sensor Networks." Journal of Computational and Theoretical Nanoscience 14, no. 1 (January 1, 2017): 685–93. http://dx.doi.org/10.1166/jctn.2017.6259.

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Wireless Multimedia Sensor network (WMSN) composed of multiple video cameras with possibly overlapping field of views. Node clustering for coordinating multimedia sensing and processing based on classical sensor clustering algorithms cannot enable wireless multimedia sensor nodes to sense areas that are uncorrelated to the areas covered by radio neighboring sensors. In this paper, a distributed clustering algorithm is proposed for WMSNs based on the coverage areas of the overlapped field of views (FoVs) and also on the direction of the FoV. A node may belong to multiple clusters, if its FoV intersects more than one cluster-head which affects efficiently in terms of energy conservation in sensing and processing. Simulation results show that our proposed algorithm has a more advantage in energy conservation, and in decreasing the number of singular nodes which impacts on the clustering efficiency and prolongs the network lifetime effectively.

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Zhou, Zhili, QM Jonathan Wu, Fang Huang, and Xingming Sun. "Fast and accurate near-duplicate image elimination for visual sensor networks." International Journal of Distributed Sensor Networks 13, no. 2 (February 2017): 155014771769417. http://dx.doi.org/10.1177/1550147717694172.

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Currently, a huge amount of visual data such as digital images and videos have been collected by visual sensor nodes, that is, camera nodes, and distributed on visual sensor networks. Among the visual data, there are a lot of near-duplicate images, which cause a serious waste of limited storage, computing, and transmission resources of visual sensor networks and a negative impact on users’ experience. Thus, near-duplicate image elimination is urgently demanded. This article proposes a fast and accurate near-duplicate elimination approach for visual sensor networks. First, a coarse-to-fine clustering method based on a combination of global feature and local feature is proposed to cluster near-duplicate images. Then in each near-duplicate group, we adopt PageRank algorithm to analyze the contextual relevance among images to select and reserve seed image and remove the others. The experimental results prove that the proposed approach achieves better performances in the aspects of both efficiency and accuracy compared with the state-of-the-art approaches.

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Tang, Mengfan, Qian Zhou, Ming Yang, Yifan Jiang, and Boyan Zhao. "Improvement of Image Stitching Using Binocular Camera Calibration Model." Electronics 11, no. 17 (August 27, 2022): 2691. http://dx.doi.org/10.3390/electronics11172691.

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Image stitching is the process of stitching several images that overlap each other into a single, larger image. The traditional image stitching algorithm searches the feature points of the image, performs alignments, and constructs the projection transformation relationship. The traditional algorithm has a strong dependence on feature points; as such, if feature points are sparse or unevenly distributed in the scene, the stitching will be misaligned or even fail completely. In scenes with obvious parallaxes, the global homography projection transformation relationship cannot be used for image alignment. To address these problems, this paper proposes a method of image stitching based on fixed camera positions and a hierarchical projection method based on depth information. The method does not depend on the number and distribution of feature points, so it avoids the complexity of feature point detection. Additionally, the effect of parallax on stitching is eliminated to a certain extent. Our experiments showed that the proposed method based on the camera calibration model can achieve more robust stitching results when a scene has few feature points, uneven feature point distribution, or significant parallax.

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Cardona, Gustavo A., Juan Ramirez-Rugeles, Eduardo Mojica-Nava, and Juan M. Calderon. "Visual victim detection and quadrotor-swarm coordination control in search and rescue environment." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2079. http://dx.doi.org/10.11591/ijece.v11i3.pp2079-2089.

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We propose a distributed victim-detection algorithm through visual information on quadrotors using convolutional neuronal networks (CNN) in a search and rescue environment. Describing the navigation algorithm, which allows quadrotors to avoid collisions. Secondly, when one quadrotor detects a possible victim, it causes its closest neighbors to disconnect from the main swarm and form a new sub-swarm around the victim, which validates the victim’s status. Thus, a formation control that permits to acquire information is performed based on the well-known rendezvous consensus algorithm. Finally, images are processed using CNN identifying potential victims in the area. Given the uncertainty of the victim detection measurement among quadrotors’ cameras in the image processing, estimation consensus (EC) and max-estimation consensus (M-EC) algorithms are proposed focusing on agreeing over the victim detection estimation. We illustrate that M-EC delivers better results than EC in scenarios with poor visibility and uncertainty produced by fire and smoke. The algorithm proves that distributed fashion can obtain a more accurate result in decision-making on whether or not there is a victim, showing robustness under uncertainties and wrong measurements in comparison when a single quadrotor performs the mission. The well-functioning of the algorithm is evaluated by carrying out a simulation using V-Rep.

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Do, Hai T., Linh H. Truong, Minh T. Nguyen, Chen-Fu Chien, Hoang T. Tran, Hoang T. Hua, Cuong V. Nguyen, Hoa T. T. Nguyen, and Nga T. T. Nguyen. "Energy-Efficient Unmanned Aerial Vehicle (UAV) Surveillance Utilizing Artificial Intelligence (AI)." Wireless Communications and Mobile Computing 2021 (October 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/8615367.

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Recently, unmanned aerial vehicles (UAVs) enhance connectivity and accessibility for civilian and military applications. A group of UAVs with on-board cameras usually monitors or collects information about designated areas. The UAVs can build a distributed network to share/exchange and to process collected sensing data before sending to a data processing center. A huge data transmission among them may cause latency and high-energy consumption. This paper deploys artificial intelligent (AI) techniques to process the video data streaming among the UAVs. Thus, each distributed UAV only needs to send a certain required information to each other. Each UAV processes data utilizing AI and only sends the data that matters to the others. The UAVs, formed as a connected network, communicate within a short communication range and share their own data to each other. Convolution neural network (CNN) technique extracts feature from images automatically that the UAVs only send the moving objects instead of the whole frames. This significantly reduces redundant information for either each UAV or the whole network and saves a huge energy consumption for the network. The UAVs can also save energy for their motion in the sensing field. In addition, a flocking control algorithm is deployed to lead the group of UAVs in the working fields and to avoid obstacles if needed. Simulation and experimental results are provided to verify the proposed algorithms in either AI-based data processing or controlling the UAVs. The results show promising points to save energy for the networks.

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Salim, Ahmed, and Hagar Ramdan. "An Efficient Distributed Collaborative Camera Actuation Algorithm for Redundant Data Elimination for Event Detection and Monitoring in Wireless Multimedia Sensor Networks." International Journal of Computer Applications 155, no. 6 (December 15, 2016): 1–10. http://dx.doi.org/10.5120/ijca2016912331.

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Said, Y., M. Barr, and H. E. Ahmed. "Design of a Face Recognition System based on Convolutional Neural Network (CNN)." Engineering, Technology & Applied Science Research 10, no. 3 (June 7, 2020): 5608–12. http://dx.doi.org/10.48084/etasr.3490.

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Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

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VASYLKIVSKYI, Mykola, Alyona KOLOMIETS, and Nazarii HRABCHAK. "RESEARCH OF FUNCTIONAL PARAMETERS OF INFOCOMMUNICATION NETWORKS 6G." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6(1) (December 29, 2022): 46–52. http://dx.doi.org/10.31891/2307-5732-2022-315-6-46-52.

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The work presents the results of research into 6G technology, which will allow integrating all types of access in one mobile phone, ensuring smooth switching between services. New options for the use of mobile networks, such as sensing and artificial intelligence, are considered, which form new indicators of the quality of the information service вЂ“ the resolution of sensing and the probability of output. For new applications, the prospects for further research into related indicators, such as flexibility and scalability to support own artificial intelligence services and network reliability level, are considered. Prospects for the development of electronic health care based on mobile communication systems with increased requirements for reliability, availability, security and confidentiality are considered. A platform for aerial mobility of various high-resolution sensors and cameras for various industries based on unmanned aerial vehicles has been investigated. The fundamental energy limitations of calculations related to information processing, which are an important step on the way to the successful deployment of 6G wireless networks, are considered. In the design of next-generation wireless communication networks, it has been determined that traditional AI optimization algorithms (such as federated learning) usually consider the bandwidth or delay of wireless connections as a weight for distributed multiprocessor data exchange, without considering the power energy bounds between different devices in different regions. This ambiguous consideration of AI energy limitations or power costs may lead to a large divergence between the design of a wireless network and the actual deployment of AI in the future. For this reason, equal importance must be attached to green AI and green communications. Therefore, when designing the architecture of the 6G information communication system, it is necessary to fully consider the impact of artificial intelligence models, algorithms and equipment on energy consumption to provide economic benefits to customers with corresponding operating costs of the system. The peculiarities of providing users with access to Internet services at any time through the same device regardless of their location, which is one of the ultimate goals of creating effective wireless networks, are discussed. The proposed concept of green AI called Oncefor-All (once and forever), according to which it is proposed to train an information communication network with further specialization during deployment will allow for efficient logical implementation on many devices, taking into account the given resource constraints. At the same time, terrestrial and non-terrestrial 6G networks will be fully integrated at the system level, ensuring the convergence of services, radio interfaces, networks and user devices. By organically combining these two access environments into one converged multi-layer heterogeneous network covering the entire globe, 6G technology will provide users with the same service. Ensuring the global delivery of mobile services will be an important aspect of the development of the 6G network.

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Song, Bi, Chong Ding, Ahmed Kamal, Jay Farrell, and Amit Roy-chowdhury. "Distributed Camera Networks." IEEE Signal Processing Magazine 28, no. 3 (May 2011): 20–31. http://dx.doi.org/10.1109/msp.2011.940441.

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Kontogiannis, Sotirios, and Christodoulos Asiminidis. "A Proposed Low-Cost Viticulture Stress Framework for Table Grape Varieties." IoT 1, no. 2 (November 4, 2020): 337–59. http://dx.doi.org/10.3390/iot1020020.

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Climate change significantly affects viticulture by reducing the production yield and the quality characteristics of its final products. In some observed cases, the consequences of climate outages such as droughts, hail and floods are absolutely devastating for the farmers and the sustained local economies. Hence, it is essential to develop new in implementation monitoring solutions that offer remote real-time surveillance, alert triggering, minimum maintenance and automated generation of incident alerts with precision responses. This paper presents a new framework and a system for vine stress monitoring called Vity-stress. The Vity-stress framework combines field measurements with precise viticulture suggestions and stress avoidance planning. The key points of the proposed framework’s system are that it is easy to develop, easy to maintain and cheap to implement applicability. Focusing on the Mediterranean cultivated table grape varieties that are strongly affected by climate change, we propose a new stress conditions monitoring system to support our framework. The proposition includes distributed field located sensors and a novel camera module implementing deep neural network algorithms to detect stress indicators. Additionally, a new wireless sensor network supported by the iBeacon protocol has been developed. The results of the sensory measurements’ data logging and imposed image detection process’s evaluation shows that the proposed system can successfully detect different stress levels in vineyards, which in turn can allow producers to identify specific areas for irrigation, thereby saving water, energy and time.

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Svatiuk, Danylo, Oksana Svatiuk, and Oleksandr Belei. "APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM." Cybersecurity: Education, Science, Technique 4, no. 8 (2020): 97–112. http://dx.doi.org/10.28925/2663-4023.2020.8.97112.

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The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

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Devarajan, D., Zhaolin Cheng, and R. J. Radke. "Calibrating Distributed Camera Networks." Proceedings of the IEEE 96, no. 10 (October 2008): 1625–39. http://dx.doi.org/10.1109/jproc.2008.928759.

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Zhou, Jincheng, Bo Liu, and Jian Gao. "A task scheduling algorithm with deadline constraints for distributed clouds in smart cities." PeerJ Computer Science 9 (April 14, 2023): e1346. http://dx.doi.org/10.7717/peerj-cs.1346.

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Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network’s data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

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Martínez, Anselmo, Lidia M. Belmonte, Arturo S. García, Antonio Fernández-Caballero, and Rafael Morales. "Facial Emotion Recognition from an Unmanned Flying Social Robot for Home Care of Dependent People." Electronics 10, no. 7 (April 6, 2021): 868. http://dx.doi.org/10.3390/electronics10070868.

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This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the message queue telemetry transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) system developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise; fear; happiness; sadness; disgust; anger; or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

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Tessens, Linda, Marleen Morbee, Hamid Aghajan, and Wilfried Philips. "Camera selection for tracking in distributed smart camera networks." ACM Transactions on Sensor Networks 10, no. 2 (January 2014): 1–33. http://dx.doi.org/10.1145/2530281.

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Devadhas Sujakumari, Praveen, and Paulraj Dassan. "Generative Adversarial Networks (GAN) and HDFS-Based Realtime Traffic Forecasting System Using CCTV Surveillance." Symmetry 15, no. 4 (March 23, 2023): 779. http://dx.doi.org/10.3390/sym15040779.

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The most crucial component of any smart city traffic management system is traffic flow prediction. It can assist a driver in selecting the most efficient route to their destination. The digitalization of closed-circuit television (CCTV) systems has resulted in more effective and capable surveillance imaging systems for security applications. The number of automobiles on the world’s highways has steadily increased in recent decades. However, road capacity has not developed at the same rate, resulting in significantly increasing congestion. The model learning mechanism cannot be guided or improved by prior domain knowledge of real-world problems. In reality, symmetrical features are common in many real-world research objects. To mitigate this severe situation, the researchers chose adaptive traffic management to make intelligent and efficient use of the current infrastructure. Data grow exponentially and become a complex item that must be managed. Unstructured data are a subset of big data that are difficult to process and have volatile properties. CCTV cameras are used in traffic management to monitor a specific point on the roadway. CCTV generates unstructured data in the form of images and videos. Because of the data’s intricacy, these data are challenging to process. This study proposes using big data analytics to transform real-time unstructured data from CCTV into information that can be shown on a web dashboard. As a Hadoop-based architectural stack that can serve as the ICT backbone for managing unstructured data efficiently, the Hadoop Distributed File System (HDFS) stores several sorts of data using the Hadoop file storage system, a high-performance integrated virtual environment (HIVE) tables, and non-relational storage. Traditional computer vision algorithms are incapable of processing such massive amounts of visual data collected in real-time. However, the inferiority of traffic data and the quality of unit information are always symmetrical phenomena. As a result, there is a need for big data analytics with machine learning, which entails processing and analyzing vast amounts of visual data, such as photographs or videos, to uncover semantic patterns that may be interpreted. As a result, smart cities require a more accurate traffic flow prediction system. In comparison to other recent methods applied to the dataset, the proposed method achieved the highest accuracy of 98.21%. In this study, we look at the construction of a secure CCTV strategy that predicts traffic from CCTV surveillance using real-time traffic prediction analysis with generative adversarial networks (GAN) and HDFS.

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Yu, Zehua, Ling Zhang, Xingyu Gao, Yang Huang, and Xiaoke Liu. "Research on Non-Pooling YOLOv5 Based Algorithm for the Recognition of Randomly Distributed Multiple Types of Parts." Sensors 22, no. 23 (November 30, 2022): 9335. http://dx.doi.org/10.3390/s22239335.

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Part cleaning is very important for the assembly of precision machinery. After cleaning, the parts are randomly distributed in the collection area, which makes it difficult for a robot to collect them. Common robots can only collect parts located in relatively fixed positions, and it is difficult to adapt these robots to collect at randomly distributed positions. Therefore, a rapid part classification method based on a non-pooling YOLOv5 network for the recognition of randomly distributed multiple types of parts is proposed in this paper; this method classifies parts from their two-dimensional images obtained using industrial cameras. We compared the traditional and non-pooling YOLOv5 networks under different activation functions. Experimental results showed that the non-pooling YOLOv5 network improved part recognition precision by 8% and part recall rate by 3% within 100 epochs of training, which helped improve the part classification efficiency. The experiment showed that the non-pooling YOLOv5 network exhibited improved classification of industrial parts compared to the traditional YOLOv5 network.

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Lenzen, Christoph, and Roger Wattenhofer. "Distributed algorithms for sensor networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1958 (January 13, 2012): 11–26. http://dx.doi.org/10.1098/rsta.2011.0212.

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Distributed algorithms are an established tool for designing protocols for sensor networks. In this paper, we discuss the relation between distributed computing theory and sensor network applications. We also present a few basic and illustrative distributed algorithms.

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Afek, Yehuda, and Eli Gafni. "Distributed Algorithms For Unidirectional Networks." SIAM Journal on Computing 23, no. 6 (December 1994): 1152–78. http://dx.doi.org/10.1137/s009753979223277x.

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Ermis, Erhan Baki, Pierre Clarot, Pierre-Marc Jodoin, and Venkatesh Saligrama. "Activity Based Matching in Distributed Camera Networks." IEEE Transactions on Image Processing 19, no. 10 (October 2010): 2595–613. http://dx.doi.org/10.1109/tip.2010.2052824.

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Parmentier, Frans-Jan W., Lennart Nilsen, Hans Tømmervik, and Elisabeth J. Cooper. "A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales." Earth System Science Data 13, no. 7 (July 29, 2021): 3593–606. http://dx.doi.org/10.5194/essd-13-3593-2021.

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Abstract. Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago with the aim of monitoring vegetation phenology. The network consists of 10 racks equipped with sensors that measure NDVI (normalized difference vegetation index), soil temperature, and moisture as well as time-lapse RGB cameras (i.e. phenocams). Three additional time-lapse cameras are placed on nearby mountains to provide an overview of the valley. We derived the vegetation index GCC (green chromatic channel) from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust time series for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing and an overview of the dataset that is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al., 2021). In addition, we provide some examples of how these data can be used to monitor different vegetation communities in the landscape.

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Nieberg, Tim. "Distributed Algorithms in Wireless Sensor Networks." Electronic Notes in Discrete Mathematics 13 (April 2003): 81–83. http://dx.doi.org/10.1016/s1571-0653(04)00444-5.

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Galil, Zvi, Gad M. Landau, and Mordechai M. Yung. "Distributed algorithms in synchronous broadcasting networks." Theoretical Computer Science 49, no. 2-3 (1987): 171–84. http://dx.doi.org/10.1016/0304-3975(87)90006-5.

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ZAJDEL, WOJCIECH, and BEN J. A. KRÖSE. "A SEQUENTIAL BAYESIAN ALGORITHM FOR SURVEILLANCE WITH NONOVERLAPPING CAMERAS." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 08 (December 2005): 977–96. http://dx.doi.org/10.1142/s0218001405004423.

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Visual surveillance in wide areas (e.g. airports) relies on sparsely distributed cameras, that is, cameras that observe nonoverlapping scenes. In this setup, multiobject tracking requires reidentification of an object when it leaves one field of view, and later appears at some other. Although similar association problems are common for multiobject tracking scenarios, in the distributed case one has to cope with asynchronous observations and cannot assume smooth motion of the objects. In this paper, we propose a method for human indoor tracking. The method is based on a Dynamic Bayes Network (DBN) as a probabilistic model for the observations. The edges of the network define the correspondences between observations of the same object. Accordingly, we derive an approximate EM-like method for selecting the most likely structure of DBN and learning model parameters. The presented algorithm is tested on a collection of real-world observations gathered by a system of cameras in an office building.

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Tsekenis, Vasileios, Charalampos K. Armeniakos, Viktor Nikolaidis, Petros S. Bithas, and Athanasios G. Kanatas. "Machine Learning-Assisted Man Overboard Detection Using Radars." Electronics 10, no. 11 (June 4, 2021): 1345. http://dx.doi.org/10.3390/electronics10111345.

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One of the most crucial emergencies that require instant action to be taken during traveling across water is the so-called man overboard (MOB). Thus, constant monitoring equipment needs to be installed for the fast notice and detection of the victim to be rescued, if an incident happens. Despite the fact that different installations such as radar sensors, thermal cameras etc., can be handy, a combination of these could be beneficial yet it would increase the complexity. Nevertheless, the full potential may be not reached yet. The key component to what needs to be done in order to achieve the utmost accuracy is artificial intelligence (AI). That is, with the aid of AI, one can deploy an automated surveillance system capable of making its own humanlike decisions regarding such incidents like MOB. To achieve this, fully organized real-time cooperation among the concerned system components is essential. The latter holds since in such dynamically changing operational environments like these, information must be distributed fast, errorless and reliably to the decision center. This study aims to analyze and demonstrate the outcome of an integrated sensor-based system that utilizes AI, implemented for ship incidents. Different machine learning algorithms were used where each one of them made use of information that originated from a cluster of radar sensors located remotely. In particular, the deployed system’s objective is to detect human motion so it can be used to protect against potentially fateful events during ship voyages.

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Liu, Yu, Xuqi Zhu, Lin Zhang, and Sung Ho Cho. "Distributed Compressed Video Sensing in Camera Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 12 (December 24, 2012): 352167. http://dx.doi.org/10.1155/2012/352167.

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Kenk, Vildana Sulić, Rok Mandeljc, Stanislav Kovačič, Matej Kristan, Melita Hajdinjak, and Janez Perš. "Visual re-identification across large, distributed camera networks." Image and Vision Computing 34 (February 2015): 11–26. http://dx.doi.org/10.1016/j.imavis.2014.11.002.

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Ukita, Norimichi. "Probabilistic-topological calibration of widely distributed camera networks." Machine Vision and Applications 18, no. 3-4 (October 6, 2006): 249–60. http://dx.doi.org/10.1007/s00138-006-0045-z.

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Yoder, Josiah, Henry Medeiros, Johnny Park, and Avinash C. Kak. "Cluster-Based Distributed Face Tracking in Camera Networks." IEEE Transactions on Image Processing 19, no. 10 (October 2010): 2551–63. http://dx.doi.org/10.1109/tip.2010.2049179.

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Devarajan, Dhanya, Richard J. Radke, and Haeyong Chung. "Distributed metric calibration of ad hoc camera networks." ACM Transactions on Sensor Networks 2, no. 3 (August 2006): 380–403. http://dx.doi.org/10.1145/1167935.1167939.

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Tian, Zhen, Jiu Long Xiong, and Qi Zhang. "Camera Calibration with Neural Networks." Applied Mechanics and Materials 29-32 (August 2010): 2762–67. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2762.

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As the existence of the many unwanted factors, such as radial distortion and decentering distortion, the model of camera imaging is actually a nonlinear one. In order to successfully realize this kind of nonlinear mapping relationship between the 3D object points and their corresponding 2D image points, neural networks were and are still used. This paper introduced the history of camera calibration with neural network, covered the three types of neural network algorithms that have been used in camera calibration and explained their advantages as well as drawbacks with experiment results. After that, two issues that should be noted before and after the use of neural network were discussed and finally, the concluding remarks were gained.

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Foerster, Klaus-Tycho, Janne H. Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. "Input-Dynamic Distributed Algorithms for Communication Networks." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 1 (February 18, 2021): 1–33. http://dx.doi.org/10.1145/3447384.

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Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic \congest model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labeled graph under batch changes. We investigate, when a batch of α edge label changes arrive, \beginitemize \item how much time as a function of α we need to update an existing solution, and \item how much information the nodes have to keep in local memory between batches in order to update the solution quickly. \enditemize Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. In particular, we derive non-trivial upper bounds for two selected, contrasting problems: maintaining a minimum spanning tree and detecting cliques.

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Foerster, Klaus-Tycho, Janne H. Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. "Input-Dynamic Distributed Algorithms for Communication Networks." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (June 22, 2022): 71–72. http://dx.doi.org/10.1145/3543516.3453923.

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Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic CONGEST model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labelled graph under batch changes. We investigate, when a batch of alpha edge label changes arrive, - how much time as a function of alpha we need to update an existing solution, and - how much information the nodes have to keep in local memory between batches in order to update the solution quickly. Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. The diverse time complexity of our model spans from constant time, through time polynomial in alpha, and to alpha time, which we show to be enough for any task.

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Iosifidis, George, Iordanis Koutsopoulos, and Georgios Smaragdakis. "Distributed Storage Control Algorithms for Dynamic Networks." IEEE/ACM Transactions on Networking 25, no. 3 (June 2017): 1359–72. http://dx.doi.org/10.1109/tnet.2016.2633370.

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Akar, Mehmet, and Robert Shorten. "Distributed Probabilistic Synchronization Algorithms for Communication Networks." IEEE Transactions on Automatic Control 53, no. 1 (February 2008): 389–93. http://dx.doi.org/10.1109/tac.2007.914224.

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Journal articles on the topic 'Distributed algorithms for camera networks'

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