A Novel Simple Visual Tracking Algorithm Based on Hashing and Deep Learning

Deep network has been proven efficient and robust to capture object features in some conditions. It still remains in the stage of classifying or detecting objects. In the field of visual tracking, deep network has not been applied widely. One of the reasons is that its time consuming made the strong method could not meet the speed need of visual tracking. A novel simple tracker is proposed to complete tracking task. A simple six-layer feed-forward backpropagation neural network is applied to capture object features. Nevertheless, this representation is not robust enough when illumination changes or drastic scale changes in dynamic condition. To improve the performance and not to increase much time spent, image perceptual hashing method is employed, which extracts low frequency information of object as its fingerprint to recognize the object from its structure. 64-bit characters are calculated by it, and they are utilized to be the bias terms of the neutral network. This leads more significant improvement for performance of extracting sufficient object features. Then we take particle filter to complete the tracking process with the proposed representation. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art tracking methods.