Abstract
Aim at improving the robustness by employing single feature tracking algorithm in visual object tracking realm, we put forward an object tracking algorithm based on the thought of game theory via multi-feature fusion. Under the framework of Mean Shift visual tracking, treating the color features and motion features expressed by optical flow as two gamers, by searching the Nash Equilibrium of their game, makes various feature's contribution reach the optimum balance, then the advantages of feature fusion are reflected better. According to experimental results, this algorithm is robust to the drastic motion of object, obstacle occlusion and background interference. This study proposes a new algorithm, which base on traditional Mean Shift algorithm and use multi-feature fusion with game theory. The algorithm shows a good tracking performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
