Patch Based Multiple Instance Learning Algorithm for Object Tracking.
To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers' parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking.
Department of Information Engineering and Automation, Hebei College of Industry and Technology, Shijiazhuang, China.
Zhenjie
0000-0002-8501-5126
Z
Wang
Department of Information Engineering and Automation, Hebei College of Industry and Technology, Shijiazhuang, China.
Lijia
0000-0002-1907-171X
L
Wang
Faculty of Electrical & Electronics Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China.
Hua
0000-0001-9826-9929
H
Zhang
D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology*; D006801:Humans; D007858:Learning; D008962:Models, Theoretical; D009862:Online Systems; D010775:Photic Stimulation