Learning for an aesthetic model for estimating the traffic state in the traffic video

Abstract

With the increasing number of vehicles running on the urban roads, the traffic jam becomes much more serious. Properly estimating the traffic jam level from traffic videos is essential for the department of transportation management and drivers. Currently, for estimating the traffic state on videos, most solutions are built on evaluating traffic flow by counting the running vehicles per time unit or detecting their moving speed. However, the main challenge of these solutions is on the vehicle tracking method, in which the vehicles are necessary to be effectively and integrally segmented from the scenes. The solutions should tradeoff the accuracy of the estimation results and the efficiency of the method. In this paper, we propose a learning-based aesthetic model to estimate the traffic state on videos. The model uses multiple video-based perceptual features about traffic state to train the random forest classifier with the labeled data, and estimates traffic state by data classification. The evaluation experiments are conducted on a testing image set, and the results show that the traffic state estimation accuracy of the proposed model is higher than 98% and the efficiency performance is achieved in real-time.