Object tracking algorithm based on deep feature

被引：0
|
作者：
Cheng X. [1 ,2 ,3 ]
Zhang Y. [1 ,3 ]
Liu Y. [1 ,3 ]
Cui J. [3 ]
Zhou L. [1 ]
机构：
[1] School of Information Science and Engineering, Southeast University, Nanjing
[2] Nanjing Marine Radar Institute, Nanjing
[3] Key Laboratory of Machine Perception of Ministry of Education, Peking University, Beijing
来源：
Zhang, Yifeng (yfz@seu.edu.cn) | 2017年 / Southeast University卷 / 47期
关键词：
Deep learning; Sparse representation; Template updating; Visual tracking;
D O I：
10.3969/j.issn.1001-0505.2017.01.001
中图分类号：
学科分类号：
摘要：
To solve the robustness problem of the motion object in the tracking process, a tracking algorithm based on deep feature is proposed. First, each frame in the video is normalized by affine transformation. Then, the object feature is extracted from the normalized image by the stacked denoising autoencoder. Because of the large dimensions of deep feature, to improve the computational efficiency, an effective dimension reduction method based on sparse representation is presented. The high dimensional features are projected into the low dimensional space by the projection matrix. The object tracking is achieved by combing the particle filter algorithm. Finally, the object information of the first frame is integrated into the updating process of the object appearance to reduce the risk of object drift during the tracking process. The experimental results show that the proposed tracking algorithm exhibits a high degree of accuracy in six video sequences, and it can stably track the object under the circumstance of occlusion, illumination change, scale variation and fast motion. © 2017, Editorial Department of Journal of Southeast University. All right reserved.
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页码：1 / 5
页数：4
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