Augmented particle samples based optimal convolutional filters for object tracking

This paper presents the augmented particle samples based optimal convolutional filters that preserve the appearance model robustness for object tracking in both temporal and spatial levels. In temporal level, augmented particle samples provided by Laplacian group reverse sparse representation exploit the potential geometrical correlation among the different patches that keep the inherent potential distribution which facilitates the update scheme of appearance model between continuous frames in the particle filtering framework. In spatial level, structural information of multi-scale patches extraction can preserve highly stable attributes that significantly improve the object representation robustness in multi-scenarios. Moreover, the optimal convolutional filters that resulted from laplacian score exploits the coherence of high similarity in both positive and negative sets effectively that can guarantee the template update procedures discriminatively. Experimental results demonstrate that the proposed approach achieves better performance on multiple dynamic scenes.