A comprehensive review on intelligent surveillance systems
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Abstract
Intelligent surveillance system (ISS) has received growing attention due to the increasing demand on security and safety. ISS is able to automatically analyze image, video, audio or other type of surveillance data without or with limited human intervention. The recent developments in sensor devices, computer vision, and machine learning have an important role in enabling such intelligent system. This paper aims to provide general overview of intelligent surveillance system and discuss some possible sensor modalities and their fusion scenarios such as visible camera (CCTV), infrared camera, thermal camera and radar. This paper also discusses main processing steps in ISS: background-foreground segmentation, object detection and classification, tracking, and behavioral analysis.
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Ibrahim, S. W. (2016). A comprehensive review on intelligent surveillance systems. Communications in Science and Technology, 1(1). https://doi.org/10.21924/cst.1.1.2016.7
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24. Benny Ping Lai Lo, Jie Sun and Sergio A.Velastin, Fusing Visual and Audio Information in a Distributed Intelligent Surveillance System for Public Transport Systems, Acta Automatica Sinica, 29 (2003) 393-407.
25. D.L. Hall and J. Llinas, An introduction to multisensor data fusion, Proceedings of the IEEE 85 (1997) 6-23.
26. E. F. Nakamura, A. A. F. Laureiro and A. C. Frery, Information fusion for wireless sensor networks: Methods, models, and classifications, ACM Comput. Surveys 39 (2007) 1–55.
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29. X. Wang, Intelligent multi-camera video surveillance: A review, Pattern Recognition Letters, 34 (2013) 3-19.
30. Y.W. Bai, Li-Sih Shen and Zong-Han Li, Design and implementation of an embedded home surveillance system by use of multiple ultrasonic sensors, IEEE Transactions on Consumer Electronics, 56 (2010) 119-124.
31. Y.W. Bai, Z. l. Xie and Z. H. Li, Design and implementation of a home embedded surveillance system with ultra-low alert power, IEEE Transactions on Consumer Electronics, 57 (2011) 153-159.
32. J.L. Castro, M. Delgado, J. Medina and M.D. Ruiz-Lozano, Intelligent surveillance system with integration of heterogeneous information for intrusion detection, Expert Systems with Applications, 38 (2011) 11182-11192.
33. A. Prati, R. Cucchiara and R. Vezzani, A multi-camera vision system for fall detection and alarm generation, Expert Systems, 24(5) 334–345.
34. F. Porikli, Y. Ivanov and T. Haga, Robust abandoned object detection using dual foregrounds, EURASIP Journal on Advances in Signal Processing, (2008) article no. 30.
35. Y.L. Chen, Bing-Fei Wu, Hao-Yu Huang and Chung-Jui Fan, A Real-Time Vision System for Nighttime Vehicle Detection and Traffic Surveillance, IEEE Transactions on Industrial Electronics 58 (2014) 2030-2044.
36. J.C.S. J. Junior, S. R. Musse and C. R. Jung, Crowd analysis using computer vision techniques: A survey, IEEE Signal Processing Magazine 27 (2010) 66-77.
37. R. Collins, A. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto and O. Hasegawa, A system for video surveillance and monitoring, Tech. Rep. CMU-RI-TR- 00-12, Robotics Institute, Carnegie Mellon University (2000).
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39. S.A. Velastin, L. Khoudour, B.P.L Lo, J. Sun and M.A. Vicencio-Silva, PRISMATICA: a multi-sensor surveillance system for public transport networks, 12th IEE International Conference on Road Transport Information and Control, London, UK , 2004, pp.19-25.
40. N. T. Siebel and S. Maybank, The ADVISOR visual surveillance system, Proc. ECCV 2004 Workshop Applications of Computer Vision, Prague, Czech Republic, 2004, pp.103 -111.
41. B. Leininger, et al. Autonomous real-time ground ubiquitous surveillance-imaging system (ARGUS-IS). Proc. SPIE 6981 (2008) 69810H.
42. V. Morellas, I. Pavlidis and P. Tsiamyrtzis, DETER: Detection of Events for Threat Evaluation and Recognition, Machine Vision and Applications, 15(2003) 29-46.
43. J. S. Kim, D. H. Yeom and Y. H. Joo, Fast and robust algorithm of tracking multiple moving objects for intelligent video surveillance systems, IEEE Transactions on Consumer Electronics 57 (2011) 1165-1170.
44. M. Valera and S. A. Velastin, Intelligent distributed surveillance systems: a review, IEEE Proceedings - Vision, Image and Signal Processing, vol. 152 (2005) 192-204.
45. P. Kumar, A. Mittal and P. Kumar, A Multimodal Audio, Visible and Infrared Surveillance System (MAVISS), 3rd IEEE International Conference on Intelligent Sensing and Information Processing (ICISIP), Bangalore, Karnataka, India, 2005, pp. 151-157.
46. Q. Cai and J.K. Aggarwal, Tracking human motion in structured environments using a distributed-camera system, IEEE Transactions on Pattern Analysis and Machine Intelligence 21 (1999) 1241-1247.
47. R.T. Collins, A.J. Lipton, H. Fujiyoshi and T. Kanade, Algorithms for cooperative multisensor surveillance, Proceedings of the IEEE 89 (2001) 1456-1477.
48. X. Wang, S. Wang and D. Bi, Distributed Visual-Target-Surveillance System in Wireless Sensor Networks, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 (2009) 1134-1146.
49. N.M. Robertson and J. Letham, Contextual person detection in multi-modal outdoor surveillance, Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, Bucharest, Romania, 2012, pp.1930-1934.
50. C. Premebida, J. Carreira, J. Batista and U. Nunes, Pedestrian detection combining rgb and dense lidar data, International Conference on Intelligent Robots and Systems, Chicago, IL, USA, 2014, pp. 4112-4117.
51. C. Stauffer and W. Grimson, Adaptive background mixture models for real-time tracking, CVPR 1999 (1999) 246-252.
52. A. Elgammal, D. Harwood and L.Davis, Non-parametric Model for Background Subtraction, ECCV, Dublin, Ireland, 2000, pp. 751-767.
53. K. Kim, T. Chalidabhongse, D. Harwood and L. Davis, Real-time Foreground-Background Segmentation using Codebook Model, Real- Time Imaging 11 (2005) 172-185.
54. B. Lee and M. Hedley, Background Estimation for Video Surveillance, Image and Vision Computing New Zealand (IVCNZ 2002), New Zealand, 2002, pp. 315-320.
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