Particle filter-based video object tracking using feature fusion in template partitions

Moving object tracking is one of the key issues in the domain of computer vision. A variety of challenges are posed while tracking the object in the real-world scenario. In this paper, we have proposed a particle filtering-based algorithm to track the moving object in a complex real-world environment having shadows, dynamic entities in the background, bad weather condition and illumination variation. Specifically, we have attempted to have an effective target and scene model by adhering to the notion of feature fusion. In this regard, a novel feature fusion strategy is proposed which is scene dependent. Besides, for an efficient target model, the template for tracking is partitioned with a view to capture the local attributes of the object. In every partition, two features, namely the local binary pattern (LBP) and the mean RGB color features, are fused in a probabilistic framework. The weights for the probabilistic fusion are computed based on the local scene dynamics. The feature that strongly favors the scene in a given template partition is assigned more weightage and vice versa in the fusion process. The combined feature over all the template partitions is used to model the target. The particles that represent the state of the object evolve through a dynamic state model to track the object. The proposed tracking algorithm is successfully tested on videos considered from LASIESTA, CDnet, and DAVIS 2016 data base and it showed improved tracking accuracy as compared to existing algorithms.