Real-time vehicle detection and tracking based on traffic scene analysis

Abstract

In this paper, upon the background of driving assistance on highway, we propose a real-time vehicle detection and tracking algorithm based on traffic scene analysis. We describe a general traffic scene analysis framework for vehicle detection and tracking based on roadside detection at first. On that basis, we present a new object detection algorithm via fusion of global classifier and part-based classifier and a vehicle detection algorithm integrating classifying confidence and local shadow. The local shadow is obtained by detecting the Maximally Stable Extremal Regions (MSER) using a multi-resolution strategy. Finally, we test our algorithm on several video sequence captured from highway and suburban roads. The test results show high efficiency and robustness when coping with environment transition, illumination variation and vehicle orientation change.