Tracking occluded targets in high-similarity background: An online training, non-local appearance model and periodic hybrid particle filter with projective transformation

Two main challenges lie in tracking the partially occluded targets in a high-similarity background: 1) similar intensities increase the difficulty of discriminating targets from the background, and 2) occlusion (illumination and shape) decreases the relativity of targets to templates. In this paper, a novel eigenspace-based hybrid particle filter tracking method combined with online non-local appearance model is proposed to track the objects under highly similar environment with occlusions. By on-line training of the templates through non-local methods to generate the active appearance model, it is more likely find the maximum-likelihood distribution correctly. The projective transformation is utilized to cover all of the possible motion factors between the templates. The extended and unscented Kalman filters are switched to update the particles according to the linearity of the motion parameters. The experiment results show the effectiveness of our algorithm while dealing with occluded target in a high-similarity background.

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