Literatura científica selecionada sobre o tema "Multi-Camera network"
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Artigos de revistas sobre o assunto "Multi-Camera network"
Wu, Yi-Chang, Ching-Han Chen, Yao-Te Chiu e Pi-Wei Chen. "Cooperative People Tracking by Distributed Cameras Network". Electronics 10, n.º 15 (25 de julho de 2021): 1780. http://dx.doi.org/10.3390/electronics10151780.
Texto completo da fonteR.Kennady, Et al. "A Nonoverlapping Vision Field Multi-Camera Network for Tracking Human Build Targets". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 3 (31 de março de 2023): 366–69. http://dx.doi.org/10.17762/ijritcc.v11i3.9871.
Texto completo da fonteZhao, Guoliang, Yuxun Zhou, Zhanbo Xu, Yadong Zhou e Jiang Wu. "Hierarchical Multi-Supervision Multi-Interaction Graph Attention Network for Multi-Camera Pedestrian Trajectory Prediction". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 4 (28 de junho de 2022): 4698–706. http://dx.doi.org/10.1609/aaai.v36i4.20395.
Texto completo da fonteSharma, Anil, Saket Anand e Sanjit K. Kaul. "Reinforcement Learning Based Querying in Camera Networks for Efficient Target Tracking". Proceedings of the International Conference on Automated Planning and Scheduling 29 (25 de maio de 2021): 555–63. http://dx.doi.org/10.1609/icaps.v29i1.3522.
Texto completo da fonteLi, Xiaolin, Wenhui Dong, Faliang Chang e Peishu Qu. "Topology Learning of Non-overlapping Multi-camera Network". International Journal of Signal Processing, Image Processing and Pattern Recognition 8, n.º 11 (30 de novembro de 2015): 243–54. http://dx.doi.org/10.14257/ijsip.2015.8.11.22.
Texto completo da fonteLiu, Xin, Herman G. J. Groot, Egor Bondarev e Peter H. N. de With. "Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Model". Electronic Imaging 2020, n.º 10 (26 de janeiro de 2020): 95–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-095.
Texto completo da fonteHe, Li, Guoliang Liu, Guohui Tian, Jianhua Zhang e Ze Ji. "Efficient Multi-View Multi-Target Tracking Using a Distributed Camera Network". IEEE Sensors Journal 20, n.º 4 (15 de fevereiro de 2020): 2056–63. http://dx.doi.org/10.1109/jsen.2019.2949385.
Texto completo da fonteLi, Yun-Lun, Hao-Ting Li e Chen-Kuo Chiang. "Multi-Camera Vehicle Tracking Based on Deep Tracklet Similarity Network". Electronics 11, n.º 7 (24 de março de 2022): 1008. http://dx.doi.org/10.3390/electronics11071008.
Texto completo da fonteTruong, Philips, Deligiannis, Abrahamyan e Guan. "Automatic Multi-Camera Extrinsic Parameter Calibration Based on Pedestrian Torsors †". Sensors 19, n.º 22 (15 de novembro de 2019): 4989. http://dx.doi.org/10.3390/s19224989.
Texto completo da fonteSumathy, R. "Face Recognition in Multi Camera Network with Sh Feature". International Journal of Modern Education and Computer Science 7, n.º 5 (8 de maio de 2015): 59–64. http://dx.doi.org/10.5815/ijmecs.2015.05.08.
Texto completo da fonteTeses / dissertações sobre o assunto "Multi-Camera network"
Zhao, Jian. "Camera Planning and Fusion in a Heterogeneous Camera Network". UKnowledge, 2011. http://uknowledge.uky.edu/ece_etds/2.
Texto completo da fonteGuillén, Alejandro. "Implementation of a Distributed Algorithm for Multi-camera Visual Feature Extraction in a Visual Sensor Network Testbed". Thesis, KTH, Kommunikationsnät, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-167415.
Texto completo da fonteJeong, Kideog. "OBJECT MATCHING IN DISJOINT CAMERAS USING A COLOR TRANSFER APPROACH". UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_theses/434.
Texto completo da fonteMacknojia, Rizwan. "Design and Calibration of a Network of RGB-D Sensors for Robotic Applications over Large Workspaces". Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23976.
Texto completo da fonteChen, Huiqin. "Registration of egocentric views for collaborative localization in security applications". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG031.
Texto completo da fonteThis work focuses on collaborative localization between a mobile camera and a static camera for video surveillance. In crowd scenes and sensitive events, surveillance involves locating the wearer of the camera (typically a security officer) and also the events observed in the images (e.g., to guide emergency services). However, the different points of view between the mobile camera (at ground level), and the video surveillance camera (located high up), along with repetitive patterns and occlusions make difficult the tasks of relative calibration and localization. We first studied how low-cost positioning and orientation sensors (GPS-IMU) could help refining the estimate of relative pose between cameras. We then proposed to locate the mobile camera using its epipole in the image of the static camera. To make this estimate robust with respect to outlier keypoint matches, we developed two algorithms: either based on a cumulative approach to derive an uncertainty map, or exploiting the belief function framework. Facing with the issue of a large number of elementary sources, some of which are incompatible, we provide a solution based on a belief clustering, in the perspective of further combination with other sources (such as pedestrian detectors and/or GPS data for our application). Finally, the individual location in the scene led us to the problem of data association between views. We proposed to use geometric descriptors/constraints, in addition to the usual appearance descriptors. We showed the relevance of this geometric information whether it is explicit, or learned using a neural network
Konda, Krishna Reddy. "Dynamic Camera Positioning and Reconfiguration for Multi-Camera Networks". Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/367752.
Texto completo da fonteKonda, Krishna Reddy. "Dynamic Camera Positioning and Reconfiguration for Multi-Camera Networks". Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1386/1/PhD-Thesis.pdf.
Texto completo da fonteDziri, Aziz. "Suivi visuel d'objets dans un réseau de caméras intelligentes embarquées". Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22610/document.
Texto completo da fonteMulti-object tracking constitutes a major step in several computer vision applications. The requirements of these applications in terms of performance, processing time, energy consumption and the ease of deployment of a visual tracking system, make the use of low power embedded platforms essential. In this thesis, we designed a multi-object tracking system that achieves real time processing on a low cost and a low power embedded smart camera. The tracking pipeline was extended to work in a network of cameras with nonoverlapping field of views. The tracking pipeline is composed of a detection module based on a background subtraction method and on a tracker using the probabilistic Gaussian Mixture Probability Hypothesis Density (GMPHD) filter. The background subtraction, we developed, is a combination of the segmentation resulted from the Zipfian Sigma-Delta method with the gradient of the input image. This combination allows reliable detection with low computing complexity. The output of the background subtraction is processed using a connected components analysis algorithm to extract the features of moving objects. The features are used as input to an improved version of GMPHD filter. Indeed, the original GMPHD do not manage occlusion problems. We integrated two new modules in GMPHD filter to handle occlusions between objects. If there are no occlusions, the motion feature of objects is used for tracking. When an occlusion is detected, the appearance features of the objects are saved to be used for re-identification at the end of the occlusion. The proposed tracking pipeline was optimized and implemented on an embedded smart camera composed of the Raspberry Pi version 1 board and the camera module RaspiCam. The results show that besides the low complexity of the pipeline, the tracking quality of our method is close to the stat of the art methods. A frame rate of 15 − 30 was achieved on the smart camera depending on the image resolution. In the second part of the thesis, we designed a distributed approach for multi-object tracking in a network of non-overlapping cameras. The approach was developed based on the fact that each camera in the network runs a GMPHD filter as a tracker. Our approach is based on a probabilistic formulation that models the correspondences between objects as an appearance probability and space-time probability. The appearance of an object is represented by a vector of m dimension, which can be considered as a histogram. The space-time features are represented by the transition time between two input-output regions in the network and the transition probability from a region to another. Transition time is modeled as a Gaussian distribution with known mean and covariance. The distributed aspect of the proposed approach allows a tracking over the network with few communications between the cameras. Several simulations were performed to validate the approach. The obtained results are promising for the use of this approach in a real network of smart cameras
Tahir, Syed Fahad. "Resource-constrained re-identification in camera networks". Thesis, Queen Mary, University of London, 2016. http://qmro.qmul.ac.uk/xmlui/handle/123456789/36123.
Texto completo da fonteYildiz, Enes. "PROVIDING MULTI-PERSPECTIVE COVERAGE IN WIRELESS MULTIMEDIA SENSOR NETWORKS". OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/717.
Texto completo da fonteLivros sobre o assunto "Multi-Camera network"
K, Aghajan Hamid, Cavallaro Andrea e ScienceDirect (Online service), eds. Multi-camera networks: Principles and applications. Amsterdam: Elsevier, AP, 2009.
Encontre o texto completo da fonteMulti-Camera Networks. Elsevier, 2009. http://dx.doi.org/10.1016/b978-0-12-374633-7.x0001-8.
Texto completo da fonteCavallaro, Andrea, e Hamid Aghajan. Multi-Camera Networks: Principles and Applications. Elsevier Science & Technology Books, 2009.
Encontre o texto completo da fonteBeach, David Michael. Multi-camera benchmark localization for mobile robot networks. 2004.
Encontre o texto completo da fonteBeach, David Michael. Multi-camera benchmark localization for mobile robot networks. 2005.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Multi-Camera network"
Koyama, Takashi, e Yusuke Gotoh. "Multi-camera Live Video Streaming over Wireless Network". In Advances in Mobile Computing and Multimedia Intelligence, 144–58. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48348-6_12.
Texto completo da fonteXing, Chang, Sichen Bai, Yi Zhou, Zhong Zhou e Wei Wu. "Coarse-to-Fine Multi-camera Network Topology Estimation". In Advances in Multimedia Information Processing – PCM 2017, 981–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77383-4_96.
Texto completo da fonteBo Bo, Nyan, Maarten Slembrouck, Peter Veelaert e Wilfried Philips. "Distributed Multi-class Road User Tracking in Multi-camera Network For Smart Traffic Applications". In Advanced Concepts for Intelligent Vision Systems, 517–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40605-9_44.
Texto completo da fonteMavrinac, Aaron, Jose Luis Alarcon Herrera e Xiang Chen. "Evaluating the Fuzzy Coverage Model for 3D Multi-camera Network Applications". In Intelligent Robotics and Applications, 692–701. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16584-9_66.
Texto completo da fonteChoudhary, Ayesha, Shubham Sharma, Indu Sreedevi e Santanu Chaudhury. "Real-Time Distributed Multi-object Tracking in a PTZ Camera Network". In Lecture Notes in Computer Science, 183–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19941-2_18.
Texto completo da fonteChen, Xiaotang, Kaiqi Huang e Tieniu Tan. "Learning the Three Factors of a Non-overlapping Multi-camera Network Topology". In Communications in Computer and Information Science, 104–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33506-8_14.
Texto completo da fonteShang, Yang, Qifeng Yu, Yong Xu, Guangwen Jiang, Xiaolin Liu, Sihua Fu, Xianwei Zhu e Xiaochun Liu. "An Innovative Multi-headed Camera Network: A Displacement-Relay Videometrics Method in Unstable Areas". In Fringe 2013, 871–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36359-7_161.
Texto completo da fonteTran, Quang Khoi, Khanh Hieu Ngo, Anh Huy Le Dinh, Lu Tien Truong, Hai Quan Tran e Anh Tuan Trinh. "Development of a Real-Time Obstacle Detection System on Embedded Computer Based on Neural Network and Multi-camera Streaming". In Intelligent Systems and Networks, 298–308. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3394-3_35.
Texto completo da fonteÇaldıran, Bekir Eren, e Tankut Acarman. "Multi-network for Joint Detection of Dynamic and Static Objects in a Road Scene Captured by an RGB Camera". In Lecture Notes in Networks and Systems, 837–51. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4960-9_63.
Texto completo da fonteSankaranarayanan, Aswin C., Rama Chellappa e Richard G. Baraniuk. "Distributed Sensing and Processing for Multi-Camera Networks". In Distributed Video Sensor Networks, 85–101. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-127-1_6.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Multi-Camera network"
Chang, I.-Cheng, Jia-Hong Yang e Yi-Hsiang Liao. "Multi-Camera Based Social Network Analysis". In 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2012. http://dx.doi.org/10.1109/iih-msp.2012.48.
Texto completo da fonteGao, Yi, Wanneng Wu, Ao Liu, Qiaokang Liang e Jianwen Hu. "Multi-Target Multi-Camera Tracking with Spatial-Temporal Network". In 2023 7th International Symposium on Computer Science and Intelligent Control (ISCSIC). IEEE, 2023. http://dx.doi.org/10.1109/iscsic60498.2023.00048.
Texto completo da fonteGanti, Raghu, Mudhakar Srivatsa e B. S. Manjunath. "Entity reconciliation in a multi-camera network". In ICDCN '16: 17th International Conference on Distributed Computing and Networking. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2833312.2849566.
Texto completo da fonteMhiri, Rawia, Hichem Maiza, Stephane Mousset, Khaled Taouil, Pascal Vasseur e Abdelaziz Bensrhair. "Obstacle detection using unsynchronized multi-camera network". In 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2015. http://dx.doi.org/10.1109/urai.2015.7358917.
Texto completo da fontePeng, Peixi, Yonghong Tian, Yaowei Wang e Tiejun Huang. "Multi-camera Pedestrian Detection with Multi-view Bayesian Network Model". In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.69.
Texto completo da fonteSchriebl, Wolfgang, Thomas Winkler, Andreas Starzacher e Bernhard Rinner. "A pervasive smart camera network architecture applied for multi-camera object classification". In 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC). IEEE, 2009. http://dx.doi.org/10.1109/icdsc.2009.5289377.
Texto completo da fontePanda, Rameswar, Abir Dasy e Amit K. Roy-Chowdhury. "Video summarization in a multi-view camera network". In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900089.
Texto completo da fonteShen, Edward, G. Peter K. Carr, Paul Thomas e Richard Hornsey. "Non-planar target for multi-camera network calibration". In 2009 IEEE Sensors. IEEE, 2009. http://dx.doi.org/10.1109/icsens.2009.5398433.
Texto completo da fonteDai, Xiaochen, e Shahram Payandeh. "Tracked Object Association in Multi-camera Surveillance Network". In 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/smc.2013.724.
Texto completo da fonteJunejo, Imran, Xiaochun Cao e Hassan Foroosh. "Geometry of a Non-Overlapping Multi-Camera Network". In 2006 IEEE International Conference on Video and Signal Based Surveillance. IEEE, 2006. http://dx.doi.org/10.1109/avss.2006.53.
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