Learning adaptive spatial regularization and aberrance repression correlation filters for visual tracking

Wang Ye; Liu Qiang; Qin Linbo; Teng Qizhi; He Xiaohai

doi:10.12086/oee.2021.200068

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Wang Y, Liu Q, Qin L B, et al. Learning adaptive spatial regularization and aberrance repression correlation filters for visual tracking[J]. Opto-Electron Eng, 2021, 48(1): 200068. doi: 10.12086/oee.2021.200068

Citation:

Wang Y, Liu Q, Qin L B, et al. Learning adaptive spatial regularization and aberrance repression correlation filters for visual tracking[J]. Opto-Electron Eng, 2021, 48(1): 200068. doi: 10.12086/oee.2021.200068

Learning adaptive spatial regularization and aberrance repression correlation filters for visual tracking

School of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610065, China

Fund Project: National Natural Science Foundation of China (61871278), International Science and Technology Cooperation and Exchange Project of Sichuan Science and Technology Department (2018HH0143), and Chengdu Industrial Cluster Collaborative Innovation Project (2016-XT00-00015-GX)

More Information

^*Corresponding author: Qin Linbo, E-mail: qing_lb@scu.edu.cn

Received Date 02 March 2020

Revised Date 28 June 2020

Published Date 15 January 2021

Abstract

Abstract

This paper proposes a correlation filter tracking algorithm based on adaptive spatial regularization and aberrance repression aiming at the problem that the spatial regularization weight of the background-aware correlation filter is fixed and does not adapt to the change of the target, and the problem that enlarging search area may introduce background noise, decreasing the discrimination ability of filters. First, FHOG features, CN features, and gray features are extracted to enhance the algorithm's ability to express the target. Second, aberrance repression terms are added to the target function to constrain the response map of the current frame, and to enhance the filter's discrimination ability to alleviate the filter model degradation. Finally, adaptive spatial regularization terms are added to the objective function to make the spatial regularization weights being updated as the objective changes, so that the filter can make full use of the target's diversity information. This paper involves experiments on the public data sets OTB-2013, OTB-2015 and VOT2016 to evaluate the proposed algorithm. The experimental results show that the speed of the algorithm used in this paper is 20 frames/s, evaluation indicators such as distance accuracy and success rate are superior to comparison algorithms, and it has good robustness in a variety of complex scenarios such as occlusion, background interference, and rotation changes.
- object tracking /
- correlation filter /
- aberrance repression /
- adaptive spatial regularization

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References

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Overview

Overview

Overview: As the application basis of intelligent video surveillance, motion analysis, human-computer interaction, spanning monitoring, behavior analysis, and UAV tracking, object tracking is one of the most important researches and one of the basic problems in the field of computer vision. Although the object tracking technology has made great progress in the past few decades, it is still a challenging task to track the target accurately and robustly in the case of deformation, occlusion, scale change, background clutter, and so on. Owing to its excellent performance, the kernel correlation filter tracking algorithm has recently become a popular research subject in the object tracking community. However, traditional correlation filter tracking methods use the properties of the cyclic matrix to transform calculations from the spatial domain to the frequency domain. Although calculation speed is improved using this method, some nonreal samples are also generated. This leads to undesired boundary effects, which reduce the discrimination ability of the filter and affect tracking performance. To some extent, these effects were alleviated by adding pre-defined spatial constraints on the filter coefficients or expanding the search area. However, such constraints are usually fixed for different objects and do not change during the tracking process, while expanding the search area can easily introduce background noise. To overcome the shortcomings of the algorithm, a tracking method based on adaptive spatial regularization and aberrance repression is proposed. First, FHOG features, CN features, and gray features are extracted to enhance the algorithm's ability to express targets. Second, the aberrance repression term is introduced into the objective function to constrain the rate of change of the current frame's response map, which can suppress the drift of the tracking box. Finally, the adaptive spatial regularization term is introduced into the objective function, which learns an effective spatial weight for a specific object and its appearance variations. The ADMM algorithm is used to solve the filter model and reduce computation time. In this study, experiments are performed on the OTB-2013, OTB-2015, and VOT2016 public databases. These databases are commonly used to evaluate the performance of tracking algorithms. It is worth mentioning that the OTB public database includes scale variation, illumination variation, occlusion, and background clutter challenges. Thus, it can accurately and objectively evaluate algorithm performance. The authors used KCF, BACF, Staple, ECO-HC, LDES, SRDCF, LMCF, DSST, etc., as comparison algorithms and used MATLAB R2017a as a programming language. Experimental results indicate that the proposed method exhibits excellent performance in tracking accuracy and robustness in complex scenes such as occlusion, background clutter, and rotation changes.