DEEP CONVOLUTIONAL PARTICLE FILTER WITH ADAPTIVE CORRELATION MAPS FOR VISUAL TRACKING

The robustness of the visual trackers based on the correlation maps generated from convolutional neural networks can be substantially improved if these maps are used to employed in conjunction with a particle filter. In this article, we present a particle filter that estimates the target size as well as the target position and that utilizes a new adaptive correlation filter to account for potential errors in the model generation. Thus, instead of generating one model which is highly dependent on the estimated target position and size, we generate a variable number of target models based on high likelihood particles, which increases in challenging situations and decreases in less complex scenarios. Experimental results on the Visual Tracker Benchmark v1.0 demonstrate that our proposed framework significantly outperforms state-of-the-art methods.