A Sifting Mechanism Based Object Localization Algorithmfor Fast Probabilistic Occupancy Map
ZHAO Zhenjie1,2, FANG Yongchun1,2, ZHANG Xuebo1,2
1. Institute of Robotics and Information Automatic System, Nankai University, Tianjin 300071, China;
2. Tianjin Key Laboratory of Intelligent Robotics, Tianjin 300071, China
A sifting mechanism based object localization algorithm for fast probabilistic occupancy map (sifted probabilistic occupancy map, SPOM) is proposed to calculate the positions of moving objects fast and accurately in typical multi-view surveillance scenarios. Specifically, an efficient sifting mechanism is designed firstly to roughly estimate the 3D positions of the moving objects according to the output of motion detection. Secondly, a proper likelihood model is set up by Bayesian method to calculate the occupancy probability of the objects for each position within the sifted region. Finally, object positions are obtained according to a pre-set threshold of probabilistic occupancy map, and particle filter is utilized to adjust the results to improve the localization accuracy. Compared with the conventional probabilistic occupancy map (POM), the proposed method can decrease the computational overload dramatically by discarding the impossible object positions through the sifting process, therefore the running speed is improved, and it simultaneously provides more accurate estimations for object positions. Based on the self-built experimental platform, comparative experiments of SPOM and POM are conducted, and the obtained results demonstrate that the proposed method can locate the moving objects more quickly and accurately.
[1] Aghajan H, Cavallaro A. Multi-camera networks:Principles and applications[M]. Burlington, USA: Academic Press, 2009.[2] 黄凯奇,陈晓棠,康运锋,等.智能视频监控技术综述[J].计算机学报,2014,38(6):1093-1118. Huang K Q, Chen X T, Kang Y F, et al. Intelligent visual surveillance: A review[J]. Chinese Journal of Computers, 38(6): 1093-1118.[3] Possegger H, Sternig S, Mauthner T, et al. Robust real-time tracking of multiple objects by volumetric mass densities[C]// IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, USA: IEEE, 2013: 2395-2402.[4] Zhang X B, Fang Y C, Sun N. Minimum-time trajectory planning for underactuated overhead crane systems with state and control constraints[J]. IEEE Transactions on Industrial Electronics, 2014, 61(12): 6915-6925. [5] Bouwmans T. Traditional and recent approaches in background modeling for foreground detection: An overview[J]. Computer Science Review, 2014, 11-12: 31-66.[6] Goyette N, Jodoin P M, Porikli F, et al. Changedetection.net: A new change detection benchmark dataset[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, USA: IEEE, 2012: 1–8.[7] Zhao Z J, Bouwmans T, Zhang X B, et al. A fuzzy background modeling approach for motion detection in dynamic backgrounds[C]//2nd International Conference on Multimedia and Signal Processing. Berlin, Germany: Springer-Verlag, 2012: 177-185.[8] Fleuret F, Berclaz J, Lengagne R, et al. Multicamera people tracking with a probabilistic occupancy map[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 267-282. [9] Krumm J, Harris S, Meyers B, et al. Multi-camera multi-person tracking for easyliving[C]//3rd IEEE International Workshop on Visual Surveillance. Piscataway, USA: IEEE, 2000: 3-10.[10] Andersen M, Andersen R S, Katsarakis N, et al. Three-dimensional adaptive sensing of people in a multi-camera setup[C]//18th European Signal Processing Conference. Piscataway, USA: IEEE, 2010: 964-968.[11] Baumgart B G. Geometric modeling for computer vision[D]. Stanford, USA: Stanford University, 1974.[12] Szeliski R. Rapid octree construction from image sequences[J]. Computer Vision, Graphics, and Image Processing, 1993, 58(1): 23-32. [13] Berclaz J, Fleuret F, Turetken E, et al. Multiple object tracking using k-shortest paths optimization[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 2011, 33(9): 1806-1819.[14] Shitrit H B, Berclaz J, Fleuret F, et al. Multi-commodity network flow for tracking multiple people[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1614-1627. [15] Yilmaz A, Javed O, Shah M. Object tracking: A survey[J]. ACM Computing Surveys, 2006, 38(4): No.13.[16] Matsunaga W, Watanabe E. Visual motion with pink noise induces predation behaviour[J]. Scientific Reports, 2012, 2: No.219.[17] Bishop C M. Pattern recognition and machine learning[M]. Berlin, Germany: Springer, 2006.[18] Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction[C]//IEEE International Conference on Pattern Recognition. Piscataway, USA: IEEE, 2004: 28-31.[19] Suzuki I, Kasami T. A distributed mutual exclusion algorithm [J]. ACM Transactions on Computer Systems, 1985, 3(4): 344-349. [20] Forsyth D A, Ponce J. Computer vision: A modern approach[M]. London, UK: Pearson, 2011.[21] Perera A G A, Srinivas C, Hoogs A, et al. Multi-object tracking through simultaneous long occlusions and split-merge conditions[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, USA: IEEE, 2006: 666-673.
