An improved MDNet target tracking algorithm

Abstract

Video tracking uses the semantic information between video image sequences to process, analyze and study the target to achieve target tracking. In response to the problems of sharp changes in target position, large deformation, and occlusion due to similar background interference, changes in lighting conditions, and different shapes of targets in the target tracking process, the tracking algorithm is less accurate and less robust in target appearance. An improved target tracking algorithm based on multi-domain network (MDNet) is proposed to solve this problem. By adding the Image-Align layer to the video target tracking task, a more accurate target value is obtained; applying the Directed Acyclic Graph-Recurrent Neural Network (DAG-RNN) by combining it with a convolutional neural network and modeling image neighborhood context dependencies for the target region to be tracked, we improve the problem that conventional networks only perform multi-layer extraction for the appearance features of the target, thus resulting in poor robustness to changes in target appearance; ROI Align layer is added after the convolution layer to speed up target feature extraction.