Nighttime Traffic Flow Analysis for Rain-Drop Tampered Cameras

Abstract

The proposed system provides a solution to analyze the traffic flow under challenging nighttime conditions when the surveillance camera is raindrop tampered. To deal with the challenging scenes, we extract effective features via salient region detection and block segmentation. We use the extracted features in the region of interest to construct a regression model to get an estimated vehicle number for each frame. The vehicle numbers in consecutive frames form a vehicle number sequence. A mapping model utilizing state transition likelihoods is proposed to acquire the desired per minute traffic flow from the vehicle number sequence. The experiments on highly challenging datasets have demonstrated that the proposed system can effectively estimate the traffic flow for rain-drop tampered highway surveillance cameras at night.