Junsuo Qu
ABSTRACT
This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change, target deformation, scale change, motion blur, and other factors. More specifically, we propose a target tracking algorithm, called Re-detection Multi-feature Fusion (RDMF), based on the fusion of Scale-adaptive kernel correlation filtering and re-detection. The target tracking algorithm trains three kernel correlation filters based on HOG, CN and LBP features, and then obtains the fusion weight of response graphs corresponding to different features based on APCE criterion, and uses weighted Average to complete the position estimation of the tracked target. In order to deal with the problem that the target is occluded and disappears in the tracking process, a random fern classifier is trained to perform re-detection when the target is occluded. After comparing the OTB-50 target tracking data set, the RDMF algorithm improves the range accuracy by 10.1% compared with SAMF algorithm, and is better than KCF, DSST, CN and other algorithms.
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Index Terms
- Long Time Target Tracking Algorithm Based on Multi Feature Fusion and Correlation Filtering
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