Real-Time Vehicle and Pedestrian Detection and Color Classification System

We propose a real-time intelligence surveillance system.Two important topics are studied, including vehicle and pedestrian detection, vehicle and pedestrian color classification. Existing pedestrian and vehicle detection algorithms utilize 2D cues of objects, such as pixel values, color and texture, shape information or motion. Some of them require heavy computation power and are thus prohibited from real-time applications. While many researchers focus on modeling objects based on 2D cues, the use of 3D cues in object detection are not well studied. In this paper we propose an algorithm that utilizes 3D cues to perform pedestrian and vehicle detection. The 3D cues of objects in a static scene monitored by a camera can be obtained using the intrinsic and extrinsic parameters of that camera. We apply a calibration-free method to estimate the camera parameters. This method simply requires users to specify 6 vertices on a cuboid in the scene. In the spect of vehicle color classification, we use Bayesian classifier to trained the decision boundaries of defined color in the HSV space, then determining the color of the object according to distribution of the the pixels in the vehicle and pedestrian images on the defined color region. Experiment results demonstrate our proposed method can work efficiently.