A Real-Time Target Tracking Algorithm Based on Improved Kernel Correlation Filter

Liu, Puqiu; Du, Jiepng; He, Sheng Yang; Ren, Guoyu

doi:10.1109/icispc53419.2021.00009

Cited by 1 publication

(1 citation statement)

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“…It uses a feature selection method based on sparse representation, which can effectively suppress noise and interference, and has good performance in complex scenes. ECO [9] introduces multi-party features in the input, uses convolutional network in deep learning to extract image features, and Color space features (Color Names) [10] to capture the color information of the target, and uses the histogram of oriented gradients to describe the texture and edge information of the target, in order to represent the appearance characteristics of the target more comprehensively. On this basis, the online learning method and multi-scale search strategy are applied to update the target model to adapt to the changes in the appearance of the target, which makes the model have good stability.…”

Section: Related Work 21 Correlation Filter-based Trackersmentioning

confidence: 99%

Anti-Similar Visual Target Tracking Algorithm Based on Filter Peak Guidance and Fusion Network

Wang

Yuan

et al. 2023

Electronics

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Visual tracking is a key research area in computer vision, as tracking technology is increasingly being applied in daily life, it has high-research significance. Visual tracking technology usually faces various challenging interference factors, among which, a similar background is one of the factors that has a greater impact on the tracking process. Kernelized Correlation Filter (KCF) tracking algorithm can track targets quickly by using circulant matrix, and has good tracking effect, so it is widely used in the tracking field. However, when the target is interfered by similar objects, the filter template in KCF cannot effectively distinguish between the target and the interfering object. This is because the filter only uses the texture gradient feature as the description object of the target, which will make the KCF algorithm extremely sensitive to the change of the target; therefore, the filter has difficultly making a judgment in the unstable scene, cannot accurately describe the target state, and finally leads to tracking failure. Therefore, this paper fuses Color Names (CN) on the basis of the original Histogram of Oriented Gradients (HOG) feature of KCF, which can obtain a more comprehensive feature representation, and realize the application of combined features to improve the anti-interference ability of KCF in complex scenes. In addition, this paper also uses the peak response of correlation filtering as the judgment condition to determine whether the current tracking result is stable. When the filter is in an unstable tracking state, the proposed algorithm will select the value with high confidence from its multiple responses as the candidate target of the Siamese network, and the deep learning network is used as the incremental learning method of the filter. The Channel Attention is introduced into the network layer, so that the network can adaptively reason and adjust the extracted universal features, and the enhanced feature information is used as the final discriminant basis. Finally, according to the response, the target with the smallest error compared with the target template is selected from multiple candidate targets as the final tracking result. The experimental results show that the average accuracy and average success rate of the proposed algorithm are significantly improved compared with the classical tracking algorithm, especially in dealing with similar target interference.

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Section: Related Work 21 Correlation Filter-based Trackersmentioning

confidence: 99%