Multifeature Fusion Tracking Algorithm Based on Self-Associative Memory Learning Mechanism

Abstract

A multi-feature fusion tracking algorithm updated with a self-associative memory learning mechanism is proposed to address the problems of short-time disappearance, re-emergence of the target and instability of single features in the kernelized correlation filtering algorithm. When extracting features, directional gradient histogram features, color features, and scale invariant features are fused instead of single features to collect more features of the target and increase the feature robustness. In the detection stage, the bimodal detection is proposed to judge whether the target model needs updating. Bimodal detection is used to judge the maximum target response in the search domain and predict the location of the target in the next frame. The self-associative memory learning mechanism was added into the updating template, and the original algorithm framework was improved to cope with the change of target model. The new algorithm update is biogenic, can recover fragment information, deal with complex and changeable tracking situation. Simulation experiments were conducted on the OTB50, OTB100, and UAV123 video datasets for the classical and new algorithms. The simulation verified that the proposed tracking algorithm has a high success and accuracy rate, which has research value. The tracking success rate improved by 23.6% and the accuracy rate improved by 18.8%.