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An improved kernel correlation filter for complex scenes target tracking
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AI
DOI：10.1007/s11042-022-12669-7.png)
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Abstract
En 中文
Video tracking technology employed to achieve efficient and accurate tracking of targets in complex scenes has often been one of the challenges to be tackled. When the target is in a complex scene similar to target interference, it will still create a series of issues, such as template drift although the current target tracking has achieved quality results in terms of accuracy, robustness, and speed. We propose an improved kernel correlation filter algorithm in response to this problem. We introduced a regularization matrix and fused properties of HOG and CN to train an improved kernel correlation filter. Furthermore, an independent scale filter is employed to regulate the scale adaptively. We have introduced a re-detection module to prevent the issue of the kernel correlation filter algorithm relying mainly on the maximum response value.A considerable number of experiments have been conducted on the aforementioned improvements. The algorithm's average tracking accuracy can attain 85.8%, in the OTB2015 dataset and its running speed can attain 198FPS. The algorithm's EAO, accuracy, and robustness, on the VOT2016 dataset, can attain 0.303, 0.553, and 0.932, respectively.Experiments demonstrate that our algorithm has satisfactory accuracy and robustness, and satisfies the real-time effect.
Keywords:
Features fusion
Re-detection
Spatial regularization
Video target tracking
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