Correlation filters based on spatial-temporal Gaussion scale mixture modelling for visual tracking

Abstract

Correlation Filters (CFs) have shown outstanding performance in tracking, but are subject to unwanted boundary effects. Spatial regularization (SR) is widely used as an efficient method to alleviate the boundary effects. However, spatial regularization is almost handcrafted and fixed during tracking process, which cannot handle the diversity of objects and the complexity of motion. Furthermore, the rich spatio-temporal correlations among multiple targets of interest cannot be fully exploited. Herein, we propose a spatio-temporal Gaussian scale mixture model (ST-GSM) for correlation-filter-based visual tracking. In our Gaussian scale mixture (GSM) model, each correlation filter coefficient is decomposed into the product of a positive scalar multiplier with sparsity and a Gaussian random variable. The reliable components of the Gaussian random variable can be adaptively selected based on the positive multipliers, aiming at alleviating the notorious boundary effects. To exploit the temporal consistency between adjacent frames, nonzero-means GSM models are developed to characterize the temporal correlations. Specifically, the filter coefficient obtained in the previous frame is used as the mean prior for the current frame. The spatial correlations among filter coefficients have been considered in the structured GSM model, thereby further improving the tracking performance. Experimental results show that the proposed model can significantly improve the performance of CF-based trackers.