Long‐time target tracking algorithm based on re‐detection multi‐feature fusion

Abstract

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, a target tracking algorithm, called re‐detection multi‐feature fusion, is proposed based on the fusion of scale‐adaptive kernel correlation filtering and re‐detection. The target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients, colour name, and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy 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 dataset, the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB‐100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.

Keywords

• machine learning
• pedestrian identification
• robustness
• visual surveillance
• visual tracking