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- Title
Enhanced Foreground Perception Correlation Filtering Target Tracking.
- Authors
JIANG Wentao; XU Xiaoqing
- Abstract
In order to alleviate the problem that the tracking accuracy of the correlation filtering target tracking algorithm is low due to the influence of deformation, fast motion, motion blur and similarity interference, this paper proposes the correlation filtering target tracking with enhanced foreground perception. An improved color histogram interference sensing model is introduced based on the correlation filtering algorithm. Firstly, based on the traditional background object model, the color difference component between foreground histogram and background histogram is enhanced to obtain a more prominent foreground color histogram interference sensing model. The correlation filter algorithm and the color histogram interference perception model are used to extract corresponding features and calculate their respective responses. Then the color histogram interference perception model is used to calculate the average probability that the pixels in the target area belong to the target. The average probability controls the fusion weights of correlation filter response and color histogram response. The maximum position of the fusion interference perception response graph is used to locate the target. Finally, the discriminant conditions of tracking anomalies are set. When abnormal conditions occur, no model update is carried out. When the tracking confidence is high, the range of target change is judged by frame difference method and Euclidean distance between two frames. The corresponding learning rate of correlation filtering template is set to realize the adaptive updating of tracking template. Experimental comparison with mainstream algorithms is conducted on OTB100 dataset, and the experimental results show that the proposed algorithm has better tracking performance and robustness than other algorithms under complex challenges such as deformation, fast motion, motion blur and similarity interference.
- Subjects
FILTERS &; filtration; TRACKING algorithms; LIGHT filters; COGNITIVE interference; EUCLIDEAN distance; HISTOGRAMS
- Publication
Journal of Frontiers of Computer Science & Technology, 2023, Vol 17, Issue 10, p2462
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2207055