Video object tracking based on SSD and camshift

Xianyu Chen; Mingru Jin; Yang Xu; Wenfeng Shen; Feng Qiu

doi:10.1117/12.2326970

29 October 2018 Video object tracking based on SSD and camshift

Xianyu Chen, Mingru Jin, Yang Xu, Wenfeng Shen, Feng Qiu

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360Z (2018) https://doi.org/10.1117/12.2326970
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

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.

Citation Download Citation

Xianyu Chen, Mingru Jin, Yang Xu, Wenfeng Shen, and Feng Qiu "Video object tracking based on SSD and camshift", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360Z (29 October 2018); https://doi.org/10.1117/12.2326970

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