CHEN Can1,2, CHEH Zhaojiong1,2, GU Yang1,2, YE Dongyi1,2
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116; 2.Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116
Abstract When partition-based correlation filtering tracking algorithm is applied to deal with scale changes and target occlusion problems, the estimation of local sub-block state tracking and the relationship between local sub-block and scale change are inaccurate.To address this issue, a scale-aware partition-based cooperative correlation filter tracking algorithm is proposed. A method of local sub-block occlusion discrimination based on time-sequence smooth constraint is adopted, and the scoring strategy of the existing algorithm is improved. A cooperative motion strategy for sub-blocks is then designed to make the occluded or the deformed sub-blocks follow the normal ones to their due positions. And the ratio between target scale and distributed location of sub-blocks aggregation and dispersion is discovered to perceive target scale changes and estimate sizes. Experiments indicate that the proposed algorithm achieves better performance.
Fund:Supported by National Natural Science Foundation of China(No.61672158), Natural Science Foundation of Fujian Province(No.2018J01798)
Corresponding Authors:
CHEN Zhaojiong, master, professor. Her research interests include intelligent image processing and computational intelligence.
About author:: CHEN Can, master student. His research interests include image processing;GU Yang, master student. Her research interests include image processing;YE Dongyi, Ph.D., professor. His resear-ch interests include computational intelligence and data mining.
[1] 黄凯奇,陈晓棠,康运锋,等.智能视频监控技术综述.计算机学报, 2015, 38(6): 1093-1118. (HUANG K Q, CHEN X T, KANG Y F, et al. Intelligent Visual Surveillance: A Review. Chinese Journal of Computers, 2015, 38(6): 1093-1118.) [2] WU Y, LIM J, YANG M H. Online Object Tracking: A Benchmark // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 2411-2418. [3] NUMMIARO K, KOLLER-MEIER E, VAN GOOL L. An Adaptive Color-Based Particle Filter. Image and Vision Computing, 2003, 21(1): 99-110. [4] COMANICIU D, RAMESH V, MEER P. Real-Time Tracking of Non-rigid Objects Using Mean Shift // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2000: 142-149. [5] AVIDAN S. Support Vector Tracking // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2001. DOI: 10.1109/CVPR.2001.990474. [6] 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. [7] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual Object Tracking Using Adaptive Correlation Filters // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2010: 2544-2550. [8] HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the Circulant Structure of Tracking-by-Detection with Kernels // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2012: 702-715. [9] DANELLJAN M, KHAN S F, FELSBERG M, et al. Adaptive Color Attributes for Real-Time Visual Tracking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 1090-1097. [10] 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. [11] WOODLEY T, STENGER B, CIPOLLA R. Tracking Using Online Feature Selection and a Local Generative Model[C/OL]. [2019-03-15]. http://bjornstenger.github.io/papers/woodley_bmvc2007.pdf. [12] NAM H, HAN B. Learning Multi-domain Convolutional Neural Networks for Visual Tracking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 4293-4302. [13] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-Convolutional Siamese Networks for Object Tracking // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 850-865. [14] DANELLJAN M, HÄGER G, KHAN F S, et al. Learning Spatially Regularized Correlation Filters for Visual Tracking // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 4310-4318. [15] LI X, LIU Q, HE Z Y, et al. A Multi-view Model for Visual Tracking via Correlation Filters. Knowledge-Based Systems, 2016, 113: 88-99. [16] LIU Q, LU X H, HE Z Y, et al. Deep Convolutional Neural Networks for Thermal Infrared Object Tracking. Knowledge-Based Systems, 2017, 134: 189-198. [17] 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 Memory Learning and Adaptive Correlation Filter Based on Subspace and Histogram. Pattern Recognition and Artificial Intelligence, 2018, 31(7): 612-624.) [18] LI Y, ZHU J K. A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2014: 254-265. [19] DANELLJAN M, HÄGER G, KHAN F S, et al. Accurate Scale Estimation for Robust Visual Tracking // Proc of the British Machine Vision Conference. Nottingham, UK: BMVA Press, 2014. DOI: 10.5244/C.28.65. [20] AKIN O, ERDEM E, ERDEM A, et al. Deformable Part-Based Tracking by Coupled Global and Local Correlation Filters. Journal of Visual Communication and Image Representation, 2016, 38: 763-774. [21] LIU T, WANG G, YANG Q X. Real-Time Part-Based Visual Tracking via Adaptive Correlation Filters // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 4902-4912. [22] 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. [23] WU Y, LIM J, YANG M H. Object Tracking Benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848. [24] LIANG P P, BLASCH E, LING H B. Encoding Color Information for Visual Tracking: Algorithms and Benchmark. IEEE Transactions on Image Processing, 2015, 24(12): 5630-5644. [25] DANELLJAN M, HÄGER G, KHAN F S, et al. Discriminative Scale Space Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(8): 1561-1575. [26] BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple: Complementary Learners for Real-Time Tracking // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 1401-1409.
2019, Vol. 32 
