Traffic Surveillance: A Review of Vision Based Vehicle Detection, Recognition and Tracking

摘要

Video-based analysis of traffic surveillance is an active area of research, which has a wide variety of applications in intelligent transport systems (ITSs). In particular, urban environments are more challenging than highways due to camera placement, background clutter, and vehicle pose or orientation variations. This paper provide a comprehensive review of the state-of-the-art video processing techniques for vehicle detection, recognition and tracking with analytical description. In this survey, we categorize vehicle detection into motion and appearance based techniques, varying from simple frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling and feature extracting. We also discuss vehicle recognition and classification utilizing vehicle attributes like color, license plate, logo and type, provide a detailed description of the advances in the field. Next we categorize tracking into model, region and features based tracing. Finally tracking algorithms including Kalman and particle filter are discussed in term of correspondence matching, filtering, estimation and dynamical models.