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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.