Anti-occlusion and re-tracking of real-time moving target based on kernelized correlation filter

Tang Xuemeng; Chen Zhiguo; Fu Yi

doi:10.12086/oee.2020.190279

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Tang X M, Chen Z G, Fu Y. Anti-occlusion and re-tracking of real-time moving target based on kernelized correlation filter[J]. Opto-Electron Eng, 2020, 47(1): 190279. doi: 10.12086/oee.2020.190279

Citation:

Tang X M, Chen Z G, Fu Y. Anti-occlusion and re-tracking of real-time moving target based on kernelized correlation filter[J]. Opto-Electron Eng, 2020, 47(1): 190279. doi: 10.12086/oee.2020.190279

Anti-occlusion and re-tracking of real-time moving target based on kernelized correlation filter

1.
School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
2.
Wuxi Research Center of Environmental Science and Engineering, Wuxi, Jiangsu 214153, China

Fund Project: Supported by National Natural Science Foundation of China (61502203), Natural Science Foundation of Jiangsu Province (BK20150122), Natural Science Research Project of Jiangsu Higher Education Institutions (17KJB520039), and Scientific Research Project of "333 High-level Talent Cultivation Project" in Jiangsu Province (BRA2018147)

More Information

Corresponding author: Chen Zhiguo, E-mail:427533@qq.com

Received Date 05 February 2019

Revised Date 06 August 2019

Published Date 01 January 2020

Abstract

Abstract

The correlation filtering algorithm determines the target position by the similarity between the template and the detection target. Since the related filtering concept is used for target tracking, it has been widely concerned, and the proposal of the kernelized correlation filter is to push this concept to a new height. The kernelized correlation filter has become a research hotspot with its high speed, high precision and high robustness. However, the kernelized correlation filter has serious defects in anti-blocking performance. In this paper, the algorithm for the anti-occlusion performance of kernelized correlation filter is improved. An improved KCF algorithm based on Sobel edge binary mode algorithm is proposed. The Sobel edge binary mode algorithm is used to weight the fusion target feature. The target's peak response intensity sidelobe value is more than the detection target is lost. Finally, the Kalman algorithm is used as the target occlusion strategy. The results show that the proposed method not only has better robustness against occlusion, but also satisfy the real-time requirements and can accurately re-tracks the target.
- KCF /
- feature fusion /
- side lobe ratio /
- Kalman prediction

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References

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Overview

Overview

Overview: Target tracking is a topic that has been discussed in depth in current academic community. Its application is quite broad, spanning monitoring, motion analysis, medical imaging, behavior recognition, monitoring and human-computer interaction. When the tracking target is occluded, the accuracy of the current algorithm is not high. Therefore, the research of target tracking algorithm is still an important topic in the field of computer vision. The kernelized correlation filter is one of the most effective methods in the target tracking algorithm. It has become a research hotspot with its high speed, high precision and high robustness. More and more experts and scholars are committed to optimizing the existing features, so that the improved algorithm can achieve good experimental results. The kernelized correlation filter mainly uses the histogram of oriented gradient (HOG) in feature extraction, and determines the target position by the similarity between the template and the detection target. However, the inherent nature of the gradient makes the histogram of oriented gradient of the target very sensitive to noise and the target cannot be tracked by using this algorithm when the target is occluded. In order to overcome these shortcomings of the algorithm, this paper proposes an improved kernelized correlation filter that combines the Sobel edge binary mode algorithm. Firstly, the Sobel edge binary mode algorithm and the histogram of oriented gradient are used to weight the fusion target feature, and the HOG edge detection is enhanced for the target feature, which makes the tracking target information more obvious. Secondly, in order to make the Kalman prediction algorithm can accurately judge the target after it is occluded, the target position obtained by the kernelized correlation filter in the unoccluded tracking process is continuously merged with the target position obtained by the Kalman algorithm. Finally, the target's peak response intensity sidelobe ratio is calculated, and the detection target is judged whether it is lost. Combined with the Kalman algorithm, the position of the next frame of the target can be predicted according to the state before the target is lost. In this paper, six sets of occlusion test videos are selected on the public database visual tracker benchmark for experiments. In order to verify the effectiveness of the proposed algorithm, the authors use Matlab2018b programming, and select DSST, ECO, KCF, LDES, SRDCF, SAMF and STRCF as a comparison algorithm, which has good performance. The final experimental results show that the proposed method improves the accuracy of the algorithm when the target is occluded.