Oriented Gradient Context for pedestrian detection

This paper presents a novel context-based feature for image encoding and object detection. The Oriented Gradient Context (OGC) descriptor represents the image in the context of different local area-based oriented gradient information. Both fine and coarse oriented gradients information about the image is captured, then different sizes of local areas with statistical oriented gradients are assembled into pair combinations to represent the gradient distribution context of the image. The features are comparatively simple but information-rich for utilization by classification algorithms. Based on the context information, the detection algorithm is relatively invariant to small shifts, translations of objects and changes in object appearance; even cases with partial occlusions and cluttered background are handled. The detection algorithm based on the proposed OGC features is shown to achieve good performance on pedestrian detection, comparable to other popular algorithms.