Improved Mean Shift Target Localization using True Background Weighted Histogram and Geometric Centroid Adjustment

Abstract

Mean Shift (MS) tracking using histogram features alone may cause inaccuracy in target localization. The problem becomes worst due to presence of mingled background features in target model representation. To improve MS target localization problem, this paper propose a spatiospectral technique. The true background features are identified in target model representation using spectral and spatial weighting and then a transformation is applied to minimize their effect in target model representation for localization improvement. The target localization is further improved by adjusting the MS estimated target position through edge based centroid re positioning. The paper also propose method of target model update for background weighted histogram based algorithms followed by weighted transformation through online feature consistency data. The proposed method is designed for single object tracking in complex scenarios and tested for comparative results with existing state of the art techniques. Experimental results on numerous challenging video sequences verify the significance of proposed technique in terms of robustness to complex background, occlusions, appearance changes, and similar color object avoidance.

Keywords

• Target tracking
• spatio-spectral technique
• Mean Shift
• Guided filter
• Target localization