Abstract: In the night,the vehicles on highway are difficult to detect because of a variety of factors,such as bad highway lighting conditions and different type of lights.To solve the problem,a video-based nighttime vehicle detection and tracking algorithm was proposed.Firstly,this algorithm combines OTSU and one dimensional maximum entropy threshol-ding algorithm to extract vehicle lights,eliminating non-vehicle lights.After that,this method makes use of temporal and spatial characteristics of light to distinguish a car with multiple lights and side-by-side vehicles.Finally,the kalman filter is used to predict and track the vehicle lights.This paper analyzed the result of the algorithm in three different application scenarios,weak light smooth traffic,normal light smooth traffic and normal light congestion traffic.The experi-mental results prove that the proposed method has high accuracy and pretty real-time performance.

Key words: Nighttime highway,Vehicle detection,Smart traffic