Vehicle Detection by a Context-Aware Multichannel Feature Pyramid

Abstract

The majority of the existing appearance-based vehicle-detection systems make use of local features for detection purposes, such as Haar-like, histograms of oriented gradients, scale-invariant feature transform, and so forth. However, these local features have limitations when dealing with illuminations, scale, shape variations, and complex background situations. It is desirable for a vehicle to have discriminative and robust features. For this purpose, a novel context-aware multichannel feature pyramid has been proposed in this paper. The main contribution of this paper is proposing two context-aware structural descriptors, termed as a context-aware difference sign transform feature and context-aware difference magnitude transform feature. An image has been tiled with a dense grid of the cells, and each cell is described by both local details and context-aware structural descriptors. The context-aware structural descriptors have the ability to capture the context-aware structural information of cells. The proposed context-aware multichannel feature pyramid is able to provide more effective features for vehicle detection. The results of the two public traffic analysis datasets show that the proposed approach leads to better performances when compared with the current state-of-the-art methods. Moreover, the experimental results are determined to be satisfactory, even for the images containing vehicles that have undergone scale variations and camera viewpoint changes, as well as for images that were photographed with complex backgrounds.