Innovative Target Tracking Method Combined Adaptive $\alpha$ -$\beta$ Filter With Robust BPNN

Abstract

Target tracking is popular in computer vision field. Although the classic BPNN completes targets tracking, its computation is complex and tracking accuracy is low when the tracking scene is uncertain or complex. To deal with the difficulties above, in this paper, we propose an innovative target tracking method combined adaptive α-β filter with robust BPNN. First, we utilize the adaptive α-β filter to compute the location region on optimal filtering parameters in the prediction stage. Of course, the novel filter reduces the region and gives effective image information to the robust BPNN that has the optimal number and weight of neurons as well as the improved learning rate. Subsequently, the network makes an accurate recognition and sends back the updated positions of targets to the filter for the next cycle. Employing the novel interactive mechanism, the numerical study and experiments indicate that the proposed method has remarkable improvement on average performance in the uncertain and complex environment.