Vision-Based Analysis for Queue Characteristics and Lane Identification

This paper presents a vision-based approach to lane identification and estimation of service rate, arrival rate, and queue saturation. The method is based on analyzing object trajectories produced. Experiments are demonstrated by applying the proposed method to different traffic scenarios: light, moderate, and heavy. The accuracy of the test is examined by comparing the queue analysis results against the ground truth. Results show that the approach is able to yield satisfactory results when the vehicle movement stays within the lane. However, the error increases when vehicle movement overlaps or switches lanes. In conclusion, the algorithm works to identify the lane membership of trajectories under different conditions. The proposed method could also be used to automate the estimation of traffic congestion levels at sections covered by surveillance cameras.