Object tracking using joint histogram of color and local rhombus pattern

Object tracking is a challenging real world problem for traffic, crime scenes, sports, etc. A feature extraction method, named as the local rhombus pattern (LRP) is proposed in this work, and it is different from the conventional local binary pattern as it extracts the local relationship of neighboring pixels itself instead of local relationship with the center pixel. The proposed method is combined with HSV (hue, saturation and value) quantized histogram, and is applied to object tracking using mean shift tracking algorithm. Experiments are carried out for road traffic and sports video, using joint histogram of LRP and HSV color space, and compared to two state-of-art approaches. The experimental results show the effectiveness of the proposed method over existing methods.