Target tracking for mobile robot platforms via object matching and background anti-matching

This study proposes a novel method for target tracking based on the combination of object matching and background anti-matching which take account of both the global property of covariance matching and local property of mean shift tracking synthetically. In the background anti-matching phrase, a certain number of background regions are extracted based on the feature of color orientation codes via an entropy filter, and the covariance matrix is adapted to match these regions to get the global motion of the background; further, the object matching is carried out by a mean-shift tracking algorithm. The proposed method is evaluated in various datasets in comparison with their counterpart algorithms; experimental results sufficiently demonstrate the effectiveness of the method proposed in this study.

Research highlights
► The global property of covariance matching and local property of mean shift are integrated.
► Covariance matrix is applied to background matching for motion compensation.
► Mean shift tracker is extended to mobile platform.
► Entropy is computed by color orientation codes to evaluate the richness of an image region.