Real-time vehicle detection in urban traffic using AdaBoost
Jongmin Park, Hyun‐Chul Choi, Se‐Young Oh · 2010
This paper proposes a method for detecting vehicles in urban traffic. The proposed method extracts vehicle candidates using AdaBoost. The candidate extraction process was speeded up further, exploiting inverse perspective transform matrix. Then the vehicle candidates were verified by the existence of vertical and horizontal edges. The detected vehicle regions were corrected by the vertical edges and shadow. Our algorithm showed the detection rate of 90.77% in urban traffic under normal lighting condition. The proposed algorithm can also detect vehicles in heavy rain. Our algorithm takes 37.13ms on average to detect vehicles in 320 by 240 images on a laptop computer (Intel ® Core™2 T7200, 2.00GHz, 1.00GB RAM).