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›› 2018, Vol. 33 ›› Issue (1): 223-236.

• Regular Paper •

### 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.

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