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Object Extraction and Classification in Video Surveillance Applications

  • Muhsin Civelek (a1) and Adnan Yazici (a2)


In this paper we review a number of methods used in video surveillance applications in order to detect and classify threats. Moreover, the use of those methods in wireless surveillance networks contributes to decreasing the energy consumption of the devices because it reduces the amount of information transferred through the network. In this paper we focus on the most popular object extraction and classification methods that are used in both wired and wireless surveillance applications. We also develop an application for identification of objects from video data by implementing the selected methods and demonstrate the performance of these methods on pre-recorded videos using the outputs of this application.



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1. Sharif, V. and Potdar, E.C. (2009) Wireless multimedia sensor network technology: a survey. Seventh IEEE International Conference on Industrial Informatics, pp. 606–610.
2. Filippopoulitis, A., Hey, L., Loukas, G., Gelenbe, E. and Timotheou, S. (2008) Emergency response simulation using wireless sensor networks. Proceedings of the First International Conference on Ambient Media and Systems (ICST), 21.
3. Dimakis, N., Filippopoulitis, A. and Gelenbe, E. (2010) Distributed building evacuation simulator for smart emergency management. The Computer Journal, 53(9), pp. 13841400.
4. Gelenbe, E. and Wu, F.-J. (2012) Large scale simulation for human evacuation and rescue. Computers and Mathematics with Applications, 64(12), pp. 38693880.
5. Vasuhi, S., Fathima, A., Shanmugam, S. and Vaidehi, S.V. (2012) Object detection and tracking in secured area with wireless and multimedia sensor network. Networked Digital Technologies (Berlin, Heidelberg: Springer), pp. 356367.
6. Öztarak, H., Akkaya, K. and Yazici, A. (2013) Efficient tracking of multiple objects in wireless multimedia sensor networks. Ad-hoc & Sensor Wireless Networks, 19(3-4), pp. 241276.
7. Joshi, K. and Darshak, T. (2012) A survey on moving object detection and tracking in video surveillance system. International Journal of Soft Computing and Engineering (IJSCE), pp. 22312307.
8. Kim, K. and Larr, D. (2011) Object detection and tracking for intelligent video surveillance. Multimedia Analysis, Processing and Communications (Berlin, Heidelberg: Springer), pp. 265288.
9. Magno, M., Tombari, F., Brunelli, D., Di Stefano, L. and Benini, L. (2009) Multimodal abandoned/removed object detection for low power video surveillance systems. IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 188–193.
10. Chen, W.T., Chen, P.Y., Lee, W.S. and Huang, C.F. (2008) Design and implementation of a real time video surveillance system with wireless sensor networks. IEEE Vehicular Technology Conference, pp. 218222.
11. Wei, L., Xiaojuan, W., Koichi, M. and An, Z.H. (2010) foreground detection based on optical flow and background subtract. International Conference on Communications, Circuits and Systems (ICCCAS), pp. 359–362.
12. Piccardi, M. (2004) Background subtraction techniques: a review. IEEE International Conference on Systems, Man and Cybernetics, pp. 3099–3104.
13. Cucchiara, R., Grana, C., Piccardi, M. and Prati, A. (2003) Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), pp. 13371342.
14. Stauffer, C. and Grimson, W.E.L. (1999) Adaptive background mixture models for real-time tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2.
15. Haralick, R.M., Sternberg, S.R. and Zhuang, X. (1987) Image Analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4), pp. 532550.
16. Chan, R.H., Ho, C.W. and Nikolova, M. (2005) Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing, 14, pp. 14791485.
17. Senthilkumaran, N. and Rajesh, R. (2009) Edge detection techniques for image segmentation – a survey of soft computing approaches. International Journal of Recent Trends in Engineering, 1(2).
18. Fu, K.S. and Mui, J. K. (1981) A survey on image segmentation. Pattern Recognition, 13(1), pp. 316.
19. Dillencourt, M.B., Samet, H. and Tamminen, M. (1992) A general approach to connected-component labeling for arbitrary image representations. Journal of the Association for Computing Machinery, 39, pp. 253280.
20. Singh, A., Sawan, S., Hanmandlu, M., Madasu, V.K. and Lovell, B.C. (2009) An abandoned object detection system based on dual background segmentation. Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 352–357.
21. Zheng, X., Xiaoshi, Y., Li, N. and Wu, H. (2009) An automatic moving object detection algorithm for video surveillance applications. International Conference on Embedded Software and Systems, pp. 541–543.
22. Poppe, C., De Bruyne, S., Paridaens, T., Lambert, P. and Van de Walle, R. (2009) Moving object detection in the H. 264/AVC compressed domain for video surveillance applications. Journal of Visual Communication and Image Representation, 20(6), pp. 428437.
23. Bayona, A., Sanmiguel, J.C. and Martínez, J.M. (2010) Stationary foreground detection using background subtraction and temporal difference in video surveillance. 17th IEEE International Conference on Image Processing (ICIP), pp. 4657–4660.
24. Tuytelaars, T. and Mikolajczyk, K. (2007) Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 3(3), pp. 177280.
25. Öztarak, H., Akkaya, K. and Yazici, A. (2013) Efficient tracking of multiple objects in wireless multimedia sensor networks. Ad-hoc & Sensor Wireless Networks, 19(3-4), pp. 241276.
26. Lin, H.Y. and Wei, J.Y. (2007) A street scene surveillance system for moving object detection, tracking and classification. IEEE Intelligent Vehicles Symposium, pp. 1077–1082.
27. Brown, L.M. (2004) View independent vehicle/person classification. Proceedings of the ACM Second International Workshop on Video Surveillance & Sensor Networks, pp. 114–123.
28. Lowe, D. (1999) Object recognition from local scale-invariant features. International Conference on Computer Vision, 2, pp. 1150–1157.
29. Bay, H., Tuytelaars, T. and Gool, L.V. (2008) SURF: speeded up robust features. Computer Vision and Image Understanding (CVIU), 110(3), pp. 346359.
30. Viola, P. and Jones, M. (2001) Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 511–518.
31. Dalal, N. and Triggs, B. (2005) Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 886–893.
32. Kim, D., Rho, S. and Hwang, E. (2012) Local feature-based multi-object recognition scheme for surveillance. Engineering Applications of Artificial Intelligence, 25(7), pp. 13731380.
33. Zhou, H., Yuan, Y. and Shi, C. (2009) Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding, 113(3), pp. 345352.
34. Lu, D. and Weng, Q. (2007) A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), pp. 823870.
35. Foresti, G.L. (1998) A real-time system for video surveillance of unattended outdoor environments. IEEE Transactions on Circuits and Systems for Video Technology, 8(6), pp. 697704.
36. Zaki, M.H., Sayed, T. and El Esawey, M. (2015) A mixed urban traffic road-users classification based on automated video data analysis. Advances in Transportation Studies, 35.
37. Tschentscher, M., Koch, C., Konig, M., Salmen, J. and Schlipsing, M. (2015) Scalable real-time parking lot classification: An evaluation of image features and supervised learning algorithms. International Joint Conference on Neural Networks (IJCNN), pp. 1–8.
38. Boragno, S., Boghossian, B., Makris, D. and Velastin, S. (2008) Object classification for real-time video-surveillance applications. Fifth International Conference on Visual Information Engineering, pp. 192–197.
39. Zhang, L., Li, S.Z., Yuan, X. and Xiang, S. (2007) Real-time object classification in video surveillance based on appearance learning. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.
40. Cramer, C. and Gelenbe, E. (2000) Video quality and traffic QoS in learning-based subsampled and receiver interpolated video sequences. IEEE Journal on Selected Areas in Communications, 18(2), pp. 150167.
41. Cramer, C., Gelenbe, E. and Bakircioglu, H. (1996) Low bit-rate video compression with neural networks and temporal sub-sampling. Proceedings of the IEEE, 84(10), pp. 15291543.
42. Naftel, A. and Khalid, S. (2006) Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space. Multimedia Systems, 12(3), pp. 227238.
43. Zhang, Z., Cai, Y., Huang, K. and Tan, T. (2007) Real-time moving object classification with automatic scene division. IEEE Conference on Computer Vision and Pattern Recognition, 5, pp. 149–152.
44. Cortes, C. and Vapnik, V. (1995) Support-vector networks. Machine Learning, 20(3), pp. 273297.
45. Munder, S. and Gavrila, M.D. (2006) An experimental study on pedestrian classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11), pp. 18631868.
46.OpenCV Website. URL:, last accessed May 2015.


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