Parallel Attentive Correlation Tracking

Psychological and cognitive findings indicate that human visual perception is attentive and selective, which may process spatial and appearance selective attentions in parallel. By reflecting some aspects of these attentions, this paper presents a novel correlation filter (CF)-based tracking approach, corresponding to processing a local and a semi-local background domains, respectively. In the local domain, inspired by the Gestalt principle of figure-ground segregation, we leverage an efficient Boolean map representation, which characterizes an image by a set of Boolean maps via randomly thresholding its color channels, yielding a location response map as a weighted sum of all Boolean maps. The Boolean maps capture the topological structures of target and its scene with different granularities, thereby enabling to effectively improve tracking of non-rectangular objects. Alternatively, in the semi-local domains, we introduce a novel distractor-resilient metric regularization into CF, which acts as a force to push distractors into negative space. Consequently, the unwanted boundary effects of CF can be effectively alleviated. Finally, both models associated with the local and the semi-local domains are seamlessly integrated into a Bayesian framework, and the tracked location is determined by maximizing its likelihood function. Extensive evaluations on the OTB50, OTB100, VOT2016, and VOT2017 tracking benchmarks demonstrate that the proposed method achieves favorable performance against a variety of state-of-the-art trackers with a speed of 45 fps on a single CPU.