Paper Info
Title
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.
Year
DOI
Venue
2022
10.1016/j.neucom.2022.04.013
Neurocomputing
Keywords
DocType
Volume
Boundary effects,Spatial-temporal correlations,Gaussian scale mixture
Journal
492
ISSN
Citations 
PageRank 
0925-2312
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Yuan Cao110.69
Guangming Shi22663184.81
weisheng dong a3170666.10
Tianzhu Zhang4170582.80
Jinjian Wu553342.70
Xue Mei Xie612926.91
Xin Li7968.05