Generalized Haar filter based CNN for object detection in traffic scenes

Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). Meanwhile, it also poses to be a demanding task due to the diversity of traffic scenes and resource limitations of the platforms for traffic scene applications. To address these issues, we present a generalized Haar filter based CNN (Convolutional Neural Network) which is suitable for the object detection tasks in traffic scenes. In this approach, we first decompose an object detection task into multiple local regression tasks. Thereafter, we handle these local regression tasks using several light and efficient networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of these deep networks are constrained to the form of generalized Haar filters. Finally, we carry out various experiments to evaluate the performance of our proposed approach in traffic scene datasets. Experimental results demonstrate that our object detection system is light and effective in comparison with the state-of-the-art.