Random projections and haar cascades for accurate real-time vehicle detection and tracking

Abstract

This paper presents a robust real-time vision framework that detects and tracks vehicles from stationary traffic cameras with certain regions of interest. The framework enables intelligent transportation and road safety applications such as road-occupancy characterization, congestion detection, traffic flow computation, and pedestrian tracking. It consists of three main modules:1) detection, 2) tracking, and 3) data association. To this end, vehicles are first detected using Haar-like features. In the second phase, a light-weight appearance-based model is built using random projections to keep track of the detected vehicles. The data association module fuses new detections and existing targets for accurate tracking. The practical value of the proposed framework is demonstrated with evaluation involving several real-world experiments and variety of challenges.