Enhanced Video Target Tracking using Kalman Filter Guided Covariance Descriptor with Gaussian Similarity Weighting

摘要

The region covariance descriptor, which includes statistical and spatial features as well as correlation between features, has been widely used for target representation in visual tracking. Robustness, enabling fusion of several features, low-computational load are powerful features of the region covariance descriptor for target representation. In this paper, we have proposed a novel approach in that isotropic Gaussian weighting and Kalman filtering is used together with the region covariance descriptor which increases performance of visual tracking in relatively complex situations such as occlusion, appearance changes etc. Experimental results demonstrate the effectiveness of this approach in terms of robust visual tracking.