Abstract To seek a solution of tracking drift and object loss resulted from environmental interference and appearance change of the object, a object tracking algorithm via object saliency and adaptive background constraint is proposed. In the tracking framework of particle filter, the pixel characteristics of the object and the extended object are firstly weighted to construct the explicit model of the object according to the principle of Bayesian saliency. Next, the background around the object is considered adaptively by exploiting the saliency of the background. Finally, by judging the current appearance state of the object, the tracking result is obtained by taking advantage of the correlation between the object and the background. Matching error is reduced by the object saliency model, while tracking accuracy is improved by the adaptive constraint of background with occluded object and changed pose. The experimental results demonstrate the proposed method with stronger robustness and higher precision for object tracking.
Fund:Supported by National Natural Science Foundation of China(No.61771386,61673318), National Science Basic Research Program of Shaanxi(No.2016JM6045), Special Project of Scientific Research Project of Education Department of Shaanxi Province(No.16JK1571)
About author:: (WANG Jing, born in 1985, Ph.D. candidate. Her research interests include video image processing and pattern recognition.) (ZHU Hong(Corresponding author), born in 1963, Ph.D., professor. Her research interests include digital image processing, intelligent video surveillance and pattern recognition.)
[1] 尹宏鹏,陈 波,柴 毅,等.基于视觉的目标检测与跟踪综述.自动化学报, 2016, 42(10): 1466-1489. (YIN H P, CHEN B, CHAI Y, et al. Vision-Based Detection and Tracking: A Review. Acta Automatica Sinica, 2016, 42(10): 1466-1489.) [2] YANG H X, SHAO L, ZHENG F, et al. Recent Advances and Trends in Visual Tracking: A Review. Neurocomputing, 2011, 74(18): 3823-3831. [3] BAO C L, WU Y, LING H B, et al. Real Time Robust L1 Tracker Using Accelerated Proximal Gradient Approach // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 1830-1837. [4] ROSS D A, LIM J, LIN R S, et al. Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision, 2008, 77(1): 125-141.
[5] DOU J F, LI J X, QIN Q, et al. Moving Object Detection Based on Incremental Learning Low Rank Representation and Spatial Constraint. Neurocomputing, 2015, 168: 382-400. [6] WANG J, ZHU H, YU S Y, et al. Object Tracking Using Color-Feature Guided Network Generalization and Tailored Feature Fusion. Neurocomputing, 2017, 238: 387-398. [7] ZHANG K H, ZHANG L, YANG M H. Real-Time Compressive Tracking // Proc of the 12th European Conference on Computer Vision. Berlin, Germany: Springer, 2012: 866-879. [8] BABENKO B, YANG M H, BELONGIE S. Robust Object Tracking with Online Multiple Instance Learning. IEEE Transactions on Pa-ttern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632. [9] ZHANG S L, YU X, SUI Y, et al. Object Tracking with Multi-view Support Vector Machines. IEEE Transactions on Multimedia, 2015, 17(3): 265-278. [10] ZHONG W, LU H C, YANG M H. Robust Object Tracking via Sparsity-Based Collaborative Model // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 1838-1845. [11] CHENG X, LI N J, ZHOU T C, et al. Object Tracking via Co-llaborative Multi-task Learning and Appearance Model Updating. Applied Soft Computing, 2015, 31: 81-90. [12] RAO G M, SATYANARAYANA C. Visual Object Target Tracking Using Particle Filter: A Survey. International Journal of Image, Graphics and Signal Processing, 2013, 5(6): 57-71. [13] 王法胜,鲁明羽,赵清杰,等.粒子滤波算法.计算机学报, 2014, 37(8): 1679-1694. (WANG F S, LU M Y, ZHAO Q J, et al. Particle Filtering Algorithm. Chinese Journal of Computers, 2014, 37(8): 1679-1694.) [14] LIU H P, SUN F C. Efficient Visual Tracking Using Particle Filter with Incremental Likelihood Calculation. Information Sciences, 2012, 195: 141-153. [15] DOU J F, LI J X. Robust Visual Tracking Base on Adaptively Multi-feature Fusion and Particle Filter. Optik-International Journal for Light and Electron Optics, 2014, 125(5): 1680-1686. [16] NING J, ZHANG L, ZHANG D, et al. Robust Mean-Shift Trac-king with Corrected Background-Weighted Histogram. IET Computer Vision, 2012, 6(1): 62-69. [17] LI A, YAN S C. Object Tracking with Only Background Cues. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(11): 1911-1919. [18] RAHTU E, KANNALA J, SALO M, et al. Segmenting Salient Objects from Images and Videos // Proc of the 11th European Conference on Computer Vision. Berlin, Germany: Springer, 2010, V: 366-379.
[19] SU Y Y, ZHAO Q J, ZHAO L J, et al. Abrupt Motion Tracking Using a Visual Saliency Embedded Particle Filter. Pattern Recognition, 2014, 47(5): 1826-1834. [20] WANG F L, ZHEN Y, ZHONG B N, et al. Robust Infrared Target Tracking Based on Particle Filter with Embedded Saliency Detection. Information Sciences, 2015, 301: 215-226. [21] LI W Y, WANG P, QIAO H. Top-Down Visual Attention Integra-ted Particle Filter for Robust Object Tracking. Signal Processing: Image Communication, 2016, 43: 28-41. [22] RAHTU E, HEIKKIL J. A Simple and Efficient Saliency Detector for Background Subtraction // Proc of the 12th IEEE International Conference on Computer Vision Workshops. Washington, USA: IEEE, 2009: 1137-1144. [23] WU Y, LIM J, YANG M H. Online Object Tracking: A Benchmark // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2013: 2411-2418. [24] KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-Learning-Detection. IEEE Transactions on Pattern Analysis and Machine Inte-lligence, 2012, 34(7): 1409-1422. [25] WANG D, LU H C, YANG M H. Online Object Tracking with Sparse Prototypes. IEEE Transactions on Image Processing, 2013, 22(1): 314-325. [26] ZHANG K H, ZHANG L, YANG M H, et al. Fast Tracking via Spatio-Temporal Context Learning[J/OL]. [2017-05-01]. https://arxiv.org/pdf/1311.1939v1.pdf. [27] 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.
2017, Vol. 30 
