Multi-object detection based on binocular stereo vision

This paper proposes a new multi-object detection system based on binocular stereo vision. Firstly, we calibrate the two cameras to get intrinsic and extrinsic parameters and transformation matrix of the two cameras. Secondly, stereo rectify and stereo match is done to get a disparity map with image pairs acquired by binocular camera synchronously. Thus 3d coordinate of the objects is obtained. We then projects these 3D points to the ground to generate a top view projection image. Lastly, we propose distance and color based Mean shift cluster approach to classify the projected points, after which the number and position of objects can be determined. Binocular stereo vision based methods can overcome the problems of object occlusion, illumination variation, and shadow interference. Experiments in both indoor and corridor scenes show the advantages of the proposed system.