Visual object tracking with online weighted chaotic multiple instance learning.

In this paper, a chaotic multiple instance learning tracker based on chaos theory for a robust and efficient online tracking is introduced. In this method, chaotic characteristics can be utilized for representing the target as well as the updating appearance model, which has not been used for the tracking task. The computational architecture of the method is organized as follows. (1) Chaotic representation: a chaotic model can capture the complex dynamics of the target region to train the weak classifiers. Our representation can balance the global and local features to handle fast motion, partial occlusion, and illumination changes. (2) Importance of instance: fractal dimension of the dynamic model can be adjusted as instance weight for efficient online learning. (3) Chaotic approximation: A robust chaotic approximation to update the appearance model is introduced, which is crucial to select the discriminative and robust features. Chaotic online learning quickly explores the feature space to update the appearance model of the target by means of a chaotic map. The experimental results reveal that the proposed method is more effective and robust than the state-of-the-art trackers on various challenging sequences. Indeed, the efficiency of the proposed method is attributed to its strong online updating of chaotic policy as well as desirable target representation of chaotic model. [ABSTRACT FROM AUTHOR]