Human detection with contour-based local motion binary patterns
This paper presents a human detection method using contour-based local motion features. The local motion is encoded using a variant of the popular Local Binary Pattern (LBP) called Non-Redundant Local Binary Pattern (NRLBP) descriptor computed on the difference image of two consecutive frames. In addition, the local motion features are extracted along the human's boundary contour. Localising features on the contours has the advantage of utilizing a precise human shape description. A motivation of the proposed method is that most of informative movements are performed on boundary contours of the body parts, e.g. legs of pedestrians. Evaluation of the proposed method was conducted on the INRIA and ETH datasets. Apart from showing the importance of motion information, experimental results also showed that localising features along the object boundary contours improves the detection performance.