A Visual Object Tracking Algorithm Based on Dynamics Pattern and Convolutional Feature

Abstract

Deep visual feature-based method has demonstrated impressive performance in visual tracking attributing to its powerful capability of visual feature representation. However, in some complex environments such as dramatic change of appearance, illumination variation and rotation, the extracted deep visual feature is insufficient for accurately characterizing the target. To solve this problem, we present an integrated tracking framework which combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). Firstly, the LSTM extracted dynamics feature of target on time sequence, resulting the state of target at present time step. With that state, the accurate preprocessed bounding box was obtained. Then, deep convolutional feature of the target was extracted using a CNN, based on the processed bounding box. Finally, the position of the target was determined based on the score of the feature. During tracking stage, in order to improve the adaptation of the network, the parameters of the network were updated using samples of the target captured while successful tracking. The experiment shows that the proposed method achieves outstanding tracking performance and robustness in cases of partial occlusion, out-of-view, motion blur and fast motion.