Object detection and tracking are critical and challenging problems in vehicle environment perception systems, and have received broad attention in recent years. A novel detection and tracking algorithm taking both accuracy and real-time performance into account is proposed in this paper. First, we employ a fusion algorithm based on stereo vision and deep learning in object detection, which achieves high accuracy using two complementary algorithms. Then, a prediction-association algorithm which uses a Kalman filter and Hungarian assignment for multiple object tracking is employed for object tracking. In addition, a detection and tracking framework based on stereo vision improves the robustness of environmental perception system. Experimental results demonstrate that the proposed algorithm has high accuracy and can meet the real-time performance requirement.
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