2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-5.8
Paper Title ROBUST VISUAL OBJECT TRACKING WITH SPATIOTEMPORAL REGULARISATION AND DISCRIMINATIVE OCCLUSION DEFORMATION
Authors Shiyong Lan, Jin Li, Shipeng Sun, Sichuan University, China; Xin Lai, Southwest Petroleum University, China; Wenwu Wang, University of Surrey, United Kingdom
SessionTEC-5: Image and Video Processing 1
LocationArea G
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Processing: Linear and nonlinear filtering of images & video
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Spatiotemporal regularized Discriminative Correlation Fil-ters (DCF) have been proposed recently for visual tracking,achieving state-of-the-art performance. However, the tracking performance of the online learning model used in this kind methods is highly dependent on the quality of the appearance feature of the target, and the target feature appearance couldbe heavily deformed due to the occlusion by other objects or the variations in their dynamic self-appearance. In this paper, we propose a new approach to mitigate these two kinds of appearance deformation. Firstly, we embed the occlusion perception block into the model update stage, then we adaptively adjust the model update according to the situation of occlusion. Secondly, we use the relatively stable colour statistics to deal with the appearance shape changes in large targets, and compute the histogram response scores as a complementary part of final correlation response. Extensive experiments are performed on four well-known datasets, i.e. OTB100, VOT-2018, UAV123, and TC128. The results show that the proposed approach outperforms the baseline DCF method, especially, on the TC128/UAV123 datasets, with a gain of over 4.05%/2.43% in mean overlap precision. We will release our code at https://github.com/SYLan2019/STDOD.