Particle Filter Based Video Object Tracking Scheme With Target Remodeling and Reinitialization and its Hardware Implementation Using Raspberry Pi

Abstract

Video object tracking in real-world scenarios is one of the challenging problems of computer vision. The issue is compounded in the presence of varying illumination conditions, dynamic entities of the background, and bad weather conditions. In this research, a particle filter based new video object tracking scheme is developed with the proposed notions of target remodeling and reinitialization. During the tracking phase, the target is remodeled in each frame to take care of the changing scene dynamics over frames. The target is remodeled by fused feature distributions chosen from the created bank of fused feature distributions having discriminating potential to differentiate the target and the background in a given frame. The fused feature bank is created by fusing two features from the set consisting of Color, LBP, and HOG features. The features are fused probabilistically where the weights are determined based on the discriminating ability of a given feature. In order to achieve high tracking accuracy, the deviation of the tracker is evaluated ineach frame using the notion of time motion history and reinitialization of the tracker position takes place when the deviation is above a preselected threshold. Besides, the proposed algorithm has been implemented successfully on a Raspberry Pi based hardware setup and thus becomes a potential candidate for real time implementation. The proposed scheme is successfully tested on videos from DAVIS 2016, LASIESTA, and CDnet 2014 databases and the tracking accuracy is found to be higher than those of the existing algorithms.