Vision-based vehicle detecting and counting for traffic flow analysis
Zhimei Zhang, Kun Liu, Feng Gao, Xianyun Li, Guodong Wang · 2016
In this paper, we present a system to detect and count the number of vehicles in traffic surveillance videos based on Fast Region-based Convolutional Network (Fast R-CNN). Fast R-CNN is a state-of-the-art object detection network, which takes an entire image and a set of object proposals as input, produces bounding-box positions with probability estimates over object classes as output. First, we fine-tune a pre-trained Fast R-CNN net with images captured from traffic videos for accuracy improvement. Second, we define a series of rules of bounding boxes screening for vehicle counting. The proposed system takes around 3 seconds per image to count vehicles on a GTX970 GPU, and then records the corresponding number of vehicles into a database for traffic flow analysis. Experimental results demonstrated that the proposed system can provide significant improvements on the detection accuracy. In addition, experiments on challenging videos with occlusions or full of vehicles show that the proposed system works effectively.