Abstract

Rising criminal activities and demand of robust security solutions, detection and tracking of every minute detail of suspicious activity or object has become a topic of interest for researchers all around the world. In this paper, we propose an approach based on Digital Image and Video processing to detect and track the motion of multiple objects during the phenomenon of occlusion and activate an alert if an object is dropped for a long period of time in the region of concentration of camera. The proposed method can be utilized in video surveillance system and the method has been verified through extensive experimentation for multiple video.

References

• M. Kalpana Chowdary , S. SuparshyaBabu, S. Susrutha Babu, Dr. Habibulla Khan. “FPGA Implementation of Moving Object Detection in Frames by Using Background Subtraction Algorithm”, International conference on Communication and Signal Processing, April 3-5, 2013, pp 1032-1036.
• K.Kinoshita, M.Enokidani, M. Izumida and K.Murakami. "Tracking of a Moving Object Using One-Dimensional Optical Flow with a Rotating Observer," 9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV'06. 5-8 Dec. 2006, pp.1 – 6.
• J. L. Barron, D. J. Fleet and S. S. Beauchemin. “Performance of optical flow technique” IJCV 12:1, 1994, pp 43-77.
• Lijing Zhang and Yingli Liang. “Motion human detection based on background Subtraction”, 2010 Second International Workshop on Education Technology and Computer Science, pp.284-287.
• Massimo Piccardi. “Background subtraction techniques: a review” 2004 IEEE International Conference on Systems, Man and Cybernetics, pp.3099-3104
• Mr.Zhi Ming Qian, Xi En Cheng, YanQiu Chen.(2014)”Automatically Detect and Track Multiple Fish Swimming in shallow Water with Frequent Occlusion”,Plos ONE 9(9):e106506,doi:10.1371/journal.pone.0106506.
• Zhang Ruling, Sun Hanxu, JiaQingxuan, Yao Fusheng. “Research on fast and accurate occlusion detection technology of augmented reality system”, Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on 13-16 July 2008, pp.111-116.
• R. Depommier and E. Dubois. “Motion Estimation with Detection of Occlusion Areas”, 1992 IEEE, pp.269-272.
• J. Konrad and E. Dubois, “Bayesian estimation of discontinuous motion in images using simulated annealing”, INRS-Telecommunications, 3 Place du Commerce,Verdum, Quebec, Canada, H3E 1H6,pp.51-60.
• Alan J. Lipton, Hironobu Fujiyoshi, Raju S. Patil, “Moving Target Classification and Tracking from Real-time Video”, Fourth IEEE Workshop on Applications of Computer Vision,1998, WACV’98 Proceedings, pp.8-14.
• C. Lawrence Zitnick and Takeo Kanade, “A Cooperative Algorithm for Stereo Matching and Occlusion Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 7, July 2000, pp.675-684.
• Karlheinz Gutjahr, HannesRaggam. “Determination of Occlusion Areas in HighResolution Remote Sensing Data”, 2nd GRSS/ISPRS Joint Workshop on “Data Fusion and Remote Sensing Over Urban Areas”, pp.191-194.
• Mao Jia-Fa, Xiao Gang, Sheng Wei-Guo, Xiaohui Liu. “A 3D occlusion tracking Model of the underwater fish”, 2015 IEEE International Conference on Electro/Information Technology (EIT), 21-23 May 2015, pp.82-86.
• Yingkun Xu, Lei Qin, Guorong Li, Qingming Huang. “An efficient occlusion detection method to improve object trackers”, 2013 20th IEEE International Conference on Image Processing (ICIP), 15-18 Sept. 2013, pp.2445-2449.
• S. V. Kothiya, K. B. Mistree. “A review on real time object tracking in video sequences”, 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 24-25 Jan. 2015.