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Proceedings Paper

Video object tracking based on SSD and camshift
Author(s): Xianyu Chen; Mingru Jin; Yang Xu; Wenfeng Shen; Feng Qiu
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Paper Abstract

After years of development, the video tracking algorithm has solved the problem of complex scenes to some extent. However, the traditional video tracking algorithm is based on the characteristics of artificial extraction. Most of them are only aimed at specific goals and scenarios. They have poor generalization ability and are not robust enough to meet the requirements of intelligent monitoring. Based on the research of video tracking technology and deep learning principles and their applications, the performance of each algorithm under different scenarios was analyzed. The deep research on video tracking technology based on deep learning was conducted and proposed a video object tracking algorithm based on the combination of deep network model SSD and Camshift.This method combines deep learning with the mainstream target tracking framework, makes full use of SSD's powerful feature expression capabilities, and shows good tracking performance in complex scenes such as occlusion, deformation, and light changes in video sequences, which has good robustness and accuracy.

Paper Details

Date Published: 29 October 2018
PDF: 7 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360Z (29 October 2018); doi: 10.1117/12.2326970
Show Author Affiliations
Xianyu Chen, Shanghai Univ. (China)
Mingru Jin, Shanghai Univ. (China)
Yang Xu, Shanghai Univ. (China)
Wenfeng Shen, Shanghai Univ. (China)
Feng Qiu, Shanghai Univ. (China)

Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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