Block Target Tracking Based on Occlusion Detection and Multi-block Position Information Fusion
CHU Jun1,2, WEI Zhen2, MIAO Jun1,3, WANG Lu1,2
1. Institute of Computer Vision, Nanchang Hangkong University, Nanchang 330063; 2. College of Software, Nanchang Hangkong University, Nanchang 330063; 3. School of Aeronautical Manufacturing Engineering, Nanchang Hangkong University, Nanchang 330063
Abstract Target tracking cannot effectively determine when the target is occluded and match the template update. Aiming at this problem, a block target tracking algorithm based on occlusion detection and multi-block position information fusion is proposed. Firstly, the target is divided into four blocks, and the four ones are combined with the target as a whole. Since the occlusion has the characteristics of local start and directivity, the ratio of correlation values between each block is calculated to determine whether and where the target is occluded. The update methods are utilized selectively, depending on whether the target is occluded. Finally, the position of the final target is determined according to each unblocked position information. The experiment indicates that the proposed algorithm can effectively determine whether the target is occluded and improve the tracking effect under occlusion.
Fund:Supported by National Natural Science Foundation of China(No.61663031,61866028,61661036), Key Research and Development Program of Jiangxi Province(No.20192BBE50073)
Corresponding Authors:
CHU Jun, Ph.D., perofessor. Her research interests include image processing and analysis, pattern recognition and computer vision.
About author:: WEI Zhen, master student. His research interests include computer vision and machine learning.MIAO Jun, Ph.D., associate professor. His research interests include computer vision, pattern recognition and machine lear-ning.WANG Lu, master, lecturer. Her research interests include image processing, pattern recognition and machine learning.
[1] 卢湖川,李佩霞,王 栋.目标跟踪算法综述.模式识别与人工智能, 2018, 31(1): 61-76. (LU H C, LI P X, WANG D. Visual Object Tracking: A Survey. Pattern Recognition and Artificial Intelligence, 2018, 31(1): 61-76.) [2] 李 娜,吴玲风,李大湘.基于相关滤波的长期跟踪算法.模式识别与人工智能, 2018, 31(10): 899-908. (LI N, WU L F, LI D X. Long-Term Tracking Algorithm Based on Correlation Filter. Pattern Recognition and Artificial Intelligence, 2018, 31(10): 899-908.) [3] 冯 棐,吴小俊, 徐天阳.基于子空间和直方图的多记忆自适应相关滤波目标跟踪算法.模式识别与人工智能, 2018, 31(7): 612-624. (FENG F, WU X J, XU T Y. Object Tracking with Multiple Memo-ry Learning and Adaptive Correlation Filter Based on Subspace and Histogram. Pattern Recognition and Artificial Intelligence, 2018, 31(7): 612-624.) [4] ZHANG T Z, JIA K, XU C S, et al. Partial Occlusion Handling for Visual Tracking via Robust Part Matching // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 1258-1265. [5] LIU T, WANG G, YANG Q X. Real-Time Part-Based Visual Tra-cking via Adaptive Correlation Filters // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 4902-4912. [6] KWON J, LEE K M. Highly Non-rigid Object Tracking via Patch-Based Dynamic Appearance Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2427-2441. [7] CAI Z W, WEN L Y, LEI Z, et al. Robust Deformable and Occ-luded Object Tracking with Dynamic Graph. IEEE Transactions on Image Processing, 2014, 23(12): 5497-5509. [8] JIA X J, LU H C, YANG M H. Visual Tracking via Coarse and Fine Structural Local Sparse Appearance Models. IEEE Transactions on Image Processing, 2016, 25(10): 4555-4564. [9] LUKEZˇICˇ A, ZAJC L C, KRISTAN M. Deformable Parts Correlation Filters for Robust Visual Tracking. IEEE Transactions on Cybernetics, 2018, 48(6): 1849-1861. [10] LIU S, ZHANG T Z, CAO X C, et al. Structural Correlation Filter for Robust Visual Tracking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 4312-4320. [11] CUI Z, XIAO S T, FENG J S, et al. Recurrently Target-Attending Tracking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 1449-1458. [12] LI Y, ZHU J K, HOI S C H. Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 353-361. [13] CEHOVIN L, KRISTAN M, LEONARDIS A. Robust Visual Tra-cking Using an Adaptive Coupled-Layer Visual Model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4): 941-953. [14] WANG X M, VALSTAR M, MARTINEZ B, et al. Tric-Track: Tracking by Regression with Incrementally Learned Cascades // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 4337-4345. [15] GAO J Y, ZHANG T Z, YANG X S, et al. P2T: Part-to-Target Tracking via Deep Regression Learning. IEEE Transactions on Image Processing, 2018, 27(6): 3074-3086. [16] WU Y, LIM J, YANG M H. Object Tracking Benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848. [17] MA C, HUANG J B, YANG X K, et al. Hierarchical Convolutional Features for Visual Tracking // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 3074-3082. [18] ZHANG J M, MA S G, SCLAROFF S. MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization // Proc of the Eu-ropean Conference on Computer Vision. Berlin, Germany: Springer, 2014, VI: 188-203. [19] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-Speed Tracking with Kernelized Correlation Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. [20] GAO J, LING H B, HU W M, et al. Transfer Learning Based Vi-sual Tracking with Gaussian Processes Regression // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2014: III: 188-203. [21] ZHONG W, LU H C, YANG M H. Robust Object Tracking via Sparse Collaborative Appearance Model. IEEE Transactions on Ima-ge Processing, 2014, 23(5): 2356-2368. [22] KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-Learning-Detection. IEEE Transactions on Software Engineering, 2011, 34(7): 1409-1422. [23] HARE S, GOLODETZ S, SAFFARI A, et al. Struck: Structured Output Tracking with Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096-2109.
2020, Vol. 33 
