To solve the problem of multiple targets' detection and tracking under the complex environment, in this paper, an improved moving objects detection method is proposed based on four inter-frame differential method and optical flow algorithm. Firstly, four inter-frame difference method is used to process the of video sequences. Then objects in the video is detected accurately by the optical flow algorithm used on light streaming video sequences. This improved method enhances the processing speed of optical flow method and reduces the effects of environment's illumination. Finally, the paper compares the proposed algorithm with particle filter, ViBe algorithm under different scenarios with different moving targets and individual number. This improved method is proved not only with good robustness, but also can work more quickly and accurately on the target detection and tracking.
Algorithm for object detection and tracking combined on four inter-frame difference and optical flow methods
First published at:Aug 01, 2018
1 Yuan G W. Research on moving objects detection and tracking methods in intelligent visual surveillance system[D]. Kunming: Yunnan University, 2012.
2 Tang X. Crowd abnormal behavior detection based on sparse coding[D]. Harbin: Harbin Institute of Technology, 2013.
3 Bai X F, Yang W, Chen P H. Improved moving object detection and tracking method[J]. Video Engineering, 2014, 38(1): 180-182.
4 Xu J B. The research of detection and tracking of moving object[D]. Wuhan: China University of Geosciences, 2007.
5 He H K, Tang N J, Li Z, et al. Research of model for dynamic object segmentation based on LBP kernel density estimation[J]. Application Research of Computers, 2012, 29(7): 2719-2721.
6 Tian X T, Guo D. Motion-compensated interpolation method based on codebook[J]. Computer Engineering, 2016, 42(9): 214-219.
7 Zhang J M, Wang B. Moving object detection under condition of fast illumination change[J]. Opto-Electronic Engineering, 2016, 43(2): 14-21. DOI:10.3969/j.issn.1003-501X.2016.02.003
张金敏, 王斌.光照快速变化条件下的运动目标检测[J].光电工程, 2016, 43(2): 14-21. DOI:10.3969/j.issn.1003-501X.2016.02.003
8 Yuan G W, Chen Z Q, Gong J, et al. A Moving object detection algorithm based on a combination of optical flow and three-frame difference[J]. Journal of Chinese Computer Systems, 2013, 34(3): 668-671.
9 Wang K, Wu M, Yao H, et al. Target detection method based on multi-frame background subtractionand cauchy model[J]. Opto-Electronic Engineering, 2016, 43(10): 12-17. DOI:10.3969/j.issn.1003-501X.2016.10.003
王凯, 吴敏, 姚辉, 等.多帧背景差与Cauchy模型融合的目标检测[J].光电工程, 2016, 43(10): 12-17. DOI:10.3969/j.issn.1003-501X.2016.10.003
10 Yu P, Wang X, Tong T L, et al. A target extraction algorithm based on GrabCut segmentation algorithm and four frame differencing[J]. Microcomputer & its Applications, 2016, 35(11): 40-42.
11 Enkelmann W. Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences[J]. Computer Vision, Graphics, and Image Processing, 1988, 43(2): 150-177. DOI:10.1016/0734-189X(88)90059-X
12 Ren K Q, Yu Q M, Luo H L. Improved algorithm of moving objects detection based on gaussian mixture model[J]. Video Engineering, 2012, 36(23): 168-171. DOI:10.3969/j.issn.1002-8692.2012.23.047
任克强, 余启明, 罗会兰.一种改进的混合高斯模型运动目标检测算法[J].电视技术, 2012, 36(23): 168-171. DOI:10.3969/j.issn.1002-8692.2012.23.047
13 Yuan B H. Research on video moving object detection and tracking[D]. Hefei: Anhui University, 2014.
14 Hu S J, Ge X W, Chen Z H. Based on corner feature KLT track panoramic mosaic algorithm[J]. Journal of System Simulation, 2007, 19(8): 1742-1744.
15 Schmidt R A, Cathey W T. Optical representations for artificial intelligence problems[C]//O-E/lase'86 Symp, Los Angeles: SPIE, 1986: 226-233.
16 Wu X G, Luo L M. An improved method of optical flow estimation[J]. Acta Electronica Sinica, 2000, 28(1): 130-131.
17 Liu D. parallel optimization for video moving object detection and tracking algorithm based on GPU[D]. Changsha: National University of Defense Technology, 2013.
18 Cai R C, Xie W H, Hao Z F, et al. Abnormal crowd detection based on multi-scale recurrent neural network[J]. Journal of Software, 2015, 26(11): 2884-2896.
Get Citation: Liu Xin, Jin Xuanhong. Algorithm for object detection and tracking combined on four inter-frame difference and optical flow methods[J]. Opto-Electronic Engineering, 2018, 48(8): 170665.