ZHU Suguo, DU Junping, REN Nan. A Novel Simple Visual Tracking Algorithm Based on Hashing and Deep Learning[J]. Chinese Journal of Electronics, 2017, 26(5): 1073-1078. doi: 10.1049/cje.2016.06.026
Citation: ZHU Suguo, DU Junping, REN Nan. A Novel Simple Visual Tracking Algorithm Based on Hashing and Deep Learning[J]. Chinese Journal of Electronics, 2017, 26(5): 1073-1078. doi: 10.1049/cje.2016.06.026

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

doi: 10.1049/cje.2016.06.026
Funds:  This work was supported by the National Basic Research Program of China (973 Program) (No.2012CB821200, No.2012CB821206), and the National Natural Science Foundation of China (No.61320106006, No.61532006, No.61502042).
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  • Corresponding author: DU Junping (corresponding author) was born in 1963. She is now a professor and Ph.D. supervisor in School of Computer Science and Technology, Beijing University of Posts and Telecommunications. Her research interests include artificial intelligence, image processing and pattern recognition. (Email:junpingdu@126.com)
  • Received Date: 2015-08-04
  • Rev Recd Date: 2015-09-08
  • Publish Date: 2017-09-10
  • 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.
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