title: Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm.
abstract: An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.
authors: Department of Information Engineering and Automation, Hebei College of Industry and Technology, Shijiazhuang 050091, China.
authors: Li Jia
authors: LJ
authors: Wang
authors: Faculty of Electrical & Electronics Engineering, Shijiazhuang Vocational Technology Institute, Shijiazhuang 050081, China.
authors: Hua
authors: H
authors: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D009862:Online Systems; D010775:Photic Stimulation; D011336:Probability