›› 2018, Vol. 33 ›› Issue (1): 223-236.doi: 10.1007/s11390-017-1764-5

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Regular Paper • Previous Articles    

Robust Visual Tracking Based on Convolutional Features with Illumination and Occlusion Handing

Kang Li1,2,3, Fa-Zhi He1,2,*, Senior Member, CCF, Hai-Ping Yu1,2   

  1. 1 State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China;
    2 School of Computer Science, Wuhan University, Wuhan 430072, China;
    3 School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China
  • Received:2016-11-15 Revised:2017-07-12 Online:2018-01-05 Published:2018-01-05
  • Contact: Fa-Zhi He E-mail:fzhe@whu.edu.cn
  • About author:Kang Li is currently an assistant professor of School of Computer and Information Engineering of Hubei University, Wuhan. He received his B.S. degree in management from Anhui University, Hefei, in 2008. He received his M.S. degree in computer science from Huazhong Normal University, Wuhan, in 2012, and his Ph.D. degree in computer science from Wuhan University, Wuhan, in 2016. His research interests are computer vision, pattern recognition, image processing and computer graphics.
  • Supported by:

    This paper is supported by the National Natural Science Foundation of China under Grant No. 61472289 and the National Key Research and Development Project of China under Grant No. 2016YFC0106305.

Visual tracking is an important area in computer vision. How to deal with illumination and occlusion problems is a challenging issue. This paper presents a novel and efficient tracking algorithm to handle such problems. On one hand, a target's initial appearance always has clear contour, which is light-invariant and robust to illumination change. On the other hand, features play an important role in tracking, among which convolutional features have shown favorable performance. Therefore, we adopt convolved contour features to represent the target appearance. Generally speaking, first-order derivative edge gradient operators are efficient in detecting contours by convolving them with images. Especially, the Prewitt operator is more sensitive to horizontal and vertical edges, while the Sobel operator is more sensitive to diagonal edges. Inherently, Prewitt and Sobel are complementary with each other. Technically speaking, this paper designs two groups of Prewitt and Sobel edge detectors to extract a set of complete convolutional features, which include horizontal, vertical and diagonal edges features. In the first frame, contour features are extracted from the target to construct the initial appearance model. After the analysis of experimental image with these contour features, it can be found that the bright parts often provide more useful information to describe target characteristics. Therefore, we propose a method to compare the similarity between candidate sample and our trained model only using bright pixels, which makes our tracker able to deal with partial occlusion problem. After getting the new target, in order to adapt appearance change, we propose a corresponding online strategy to incrementally update our model. Experiments show that convolutional features extracted by well-integrated Prewitt and Sobel edge detectors can be efficient enough to learn robust appearance model. Numerous experimental results on nine challenging sequences show that our proposed approach is very effective and robust in comparison with the state-of-the-art trackers.

[1] Wang N Y, Shi J P, Yeung D Y, Jia J Y. Understanding and diagnosing visual tracking systems. In Proc. IEEE Int. Conf. Computer Vision, December 2015, pp.3101-3109.

[2] Zhang X Q, Hu W M, Bao H J, Maybank S. Robust head tracking based on multiple cues fusion in the kernelBayesian framework. IEEE Trans. Circuits and Systems for Video Technology, 2013, 23(7):1197-1208.

[3] Zhang X Q, Hu W M, Xie N H, Bao H J, Maybank S. A robust tracking system for low frame rate video. International Journal of Computer Vision, 2015, 115(3):279-304.

[4] Zhang X Q, Hu W M, Qu W, Maybank S. Multiple object tracking via species-based particle swarm optimization. IEEE Trans. Circuits and Systems for Video Technology, 2010, 20(11):1590-1602.

[5] Sun J, He F Z, Chen Y L, Chen X. A multiple template approach for robust tracking of fast motion target. Applied Mathematics-A Journal of Chinese Universities, 2016, 31(2):177-197.

[6] Grabner H, Grabner M, Bischof H. Realtime tracking via on-line boosting. In Proc. British Machine Vision Conf., September 2006, pp.47-56.

[7] Babenko B, Yang M H, Belongie S. Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Analysis and Machine Intelligence, 2011, 33(8):1619-1632.

[8] Hare S, Golodetz S, Saffari A, Vineet V, Cheng M M, Hicks S L, Torr P H S. Struck:Structured output tracking with kernels. IEEE Trans. Pattern Analysis and Machine Intelligence, 2016, 38(10):2096-2109.

[9] Mei X, Ling H B. Robust visual tracking using l1 minimization. In Proc. IEEE 12th Int. Conf. Computer Vision, September 2009, pp.1436-1443.

[10] Li K, He F Z, Yu H P, Chen X. A correlative classifiers approach based on particle filter and sample set for tracking occluded target. Applied Mathematics-A Journal of Chinese Universities, 2017, 32(3):294-312

[11] Zhong W, Lu H C, Yang M H. Robust object tracking via sparsity-based collaborative model. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2012, pp.1838-1845.

[12] Wu Y Q, He F Z, Zhang D J, Li X X. Service-oriented feature-based data exchange for cloud-based design and manufacturing. IEEE Trans. Services Computing, 2015, PP(99). doi:10.1109/TSC.2015.2501981.

[13] Bao C L, Wu Y, Ling H B, Ji H. Real time robust L1 tracker using accelerated proximal gradient approach. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2012, pp.1830-1837.

[14] Ross D A, Lim J, Lin R S, Yang M H. Incremental learning for robust visual tracking. International Journal of Computer Vision, 2008, 77(1/2/3):125-141.

[15] Kwon J, Lee K M. Visual tracking decomposition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010, pp.1269-1276.

[16] Zhang K H, Zhang L, Yang M H. Real-time compressive tracking. In Proc. the 12th European Conf. Computer Vision, October 2012, pp.864-877.

[17] Ojala T, Pietikainen M, Mäenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.

[18] Li K, He F Z, Chen X. Real-time object tracking via compressive feature selection. Frontiers of Computer Science, 2016, 10(4):689-701.

[19] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, December 2001, pp.I-511-I-518.

[20] Ni B, He F Z, Pan Y T, Yuan Z Y. Using shapes correlation for active contour segmentation of uterine fibroid ultrasound images in computer-aided therapy. Applied Mathematics-A Journal of Chinese Universities, 2016, 31(1):37-52.

[21] Zhang D J, He F Z, Han S, Zou L, Wu Y Q, Chen Y L. An efficient approach to directly compute the exact Hausdorff distance for 3D point sets. Integrated Computer Aided Engineering, 2017, 24(3):261-277.

[22] Chen Y L, He F Z, Wu Y Q, Hou N. A local start search algorithm to compute exact Hausdorff distance for arbitrary point sets. Pattern Recognition, 2017, 67:139-148

[23] Li K, He F Z, Yu H, Chen X. A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning. Frontiers of Computer Science. doi:10.1007/s11704-018-6442-4

[24] Zhang D J, He F Z, Han S H, Li X X. Quantitative optimization of interoperability during feature-based data exchange. Integrated Computer Aided Engineering, 2016, 23(1):31-50.

[25] Wang L, Liu T, Wang G, Chan K L, Yang Q X. Video tracking using learned hierarchical features. IEEE Trans. Image Processing, 2015, 24(4):1424-1435.

[26] Ma C, Huang J B, Yang X K, Yang M H. Hierarchical convolutional features for visual tracking. In Proc. IEEE Int. Conf. Computer Vision, December 2015, pp.3074-3082.

[27] Wang L J, Ouyang W L, Wang X G, Lu H C. Visual tracking with fully convolutional networks. In Proc. IEEE Int. Conf. Computer Vision, December 2015, pp.3119-3127.

[28] Wolffsohn J S, Mukhopadhyay D, Rubinstein M. Image enhancement of real-time television to benefit the visually impaired. American Journal of Ophthalmology, 2007, 144(3):436-440.

[29] Raheja J L, Kumar U. Human facial expression detection from detected in captured image using back propagation neural network. International Journal of Computer Science & Information Technology, 2010, 2(1):116-123.

[30] Sevilla-Lara L, Learned-Miller E. Distribution fields for tracking. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2012, pp.1910-1917.

[31] Oron S, Bar-Hillel A, Levi D, Avidan S. Locally orderless tracking. International Journal of Computer Vision, 2015, 111(2):213-228.

[32] Kalal Z, Mikolajczyk K, Matas J. Tracking-learningdetection. IEEE Trans. Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422.

[33] Liu B Y, Huang J Z, Yang L, Kulikowsk C. Robust tracking using local sparse appearance model and K-selection. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2011, pp.1313-1320.

[34] Henriques J F, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. In Proc. the 12th European Conf. Computer Vision, October 2012, pp.702-715.

[35] Wang D, Lu H C, Yang M H. Least soft-threshold squares tracking. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2013, pp.2371-2378.

[36] Li Y, Zhu J K, Hoi S C H. Reliable patch trackers:Robust visual tracking by exploiting reliable patches. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2015, pp.353-361.

[37] Sun C, Wang D, Lu H C. Occlusion-aware fragment-based tracking with spatial-temporal consistency. IEEE Trans. Image Processing, 2016, 25(8):3814-3825.

[38] Lv X, He F Z, Cai W W, Cheng Y. A string-wise CRDT algorithm for smart and large-scale collaborative editing systems. Advanced Engineering Informatics, 2017, 33:397-409

[39] Zhou Y, He F Z, Qiu Y M. Optimization of parallel iterated local search algorithms on graphics processing unit. The Journal of Supercomputing, 2016, 72(6):2394-2416.

[40] Zhou Y, He F Z, Qiu Y M. Dynamic strategy based parallel ant colony optimization on GPUs for TSPs. Science China Information Sciences, 2017, 60:068102.

[41] Yan X H, He F Z, Hou N, Ai H J. An efficient particle swarm optimization for largescale hardware/software codesign system. International Journal of Cooperative Information Systems. doi:10.1142/S0218843017410015.

[42] Cheng Y, He F Z, Wu Y Q, Zhang D J. Meta-operation conflict resolution for human-human interaction in collaborative feature-based CAD systems. Cluster Computing, 2016, 19(1):237-253.

[43] Yan X H, He F Z, Chen Y L. A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization. Journal of Computer Science and Technology, 2017, 32(2):340-355.
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[1] Shen Li; Stephen Y.H.Su;. Generalized Parallel Signature Analyzers with External Exclusive-OR Gates[J]. , 1986, 1(4): 49 -61 .
[2] Zhang Bo; Zhang Ling;. Statistical Heuristic Search[J]. , 1987, 2(1): 1 -11 .
[3] Meng Liming; Xu Xiaofei; Chang Huiyou; Chen Guangxi; Hu Mingzeng; Li Sheng;. A Tree-Structured Database Machine for Large Relational Database Systems[J]. , 1987, 2(4): 265 -275 .
[4] Lin Qi; Xia Peisu;. The Design and Implementation of a Very Fast Experimental Pipelining Computer[J]. , 1988, 3(1): 1 -6 .
[5] Sun Chengzheng; Tzu Yungui;. A New Method for Describing the AND-OR-Parallel Execution of Logic Programs[J]. , 1988, 3(2): 102 -112 .
[6] Tai Juwei; Wang Jue; Chen Xin;. A Syntactic-Semantic Approach for Pattern Recognition and Knowledge Representation[J]. , 1988, 3(3): 161 -172 .
[7] Zhang Bo; Zhang Tian; Zhang Jianwei; Zhang Ling;. Motion Planning for Robots with Topological Dimension Reduction Method[J]. , 1990, 5(1): 1 -16 .
[8] Wang Dingxing; Zheng Weimin; Du Xiaoli; Guo Yike;. On the Execution Mechanisms of Parallel Graph Reduction[J]. , 1990, 5(4): 333 -346 .
[9] Cai Shijie; Zhang Fuyan;. A Fast Algorithm for Polygon Operations[J]. , 1991, 6(1): 91 -96 .
[10] Zhou Quan; Wei Daozheng;. A Complete Critical Path Algorithm for Test Generation of Combinational Circuits[J]. , 1991, 6(1): 74 -82 .

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