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

作　　者：ZHU Suguo DU Junping REN Nan 

机构地区：[1]Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia,School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《Chinese Journal of Electronics》2017年第5期1073-1078,共6页电子学报（英文版）

基　　金：supported by the National Basic Research Program of China(973 Program)(No.2012CB821200,No.2012CB821206);the National Natural Science Foundation of China(No.61320106006,No.61532006,No.61502042)

摘　　要：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.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.

关 键 词：Visual tracking Image perceptual hashing Deep learning Neural network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]