Accurate Speed Measurement from Vehicle Trajectories using AdaBoost Detection and Robust Template Tracking

摘要

Image streams from road surveillance cameras often show a poor quality for measuring the vehicle驴s speed. This paper discusses methods for detection and tracking of vehicles that 1) provides statistical data on bidirectional traffic and 2) provides continuous speed measurements of individual vehicles for a typical range up to 100m for rear viewed and 70m for oncoming vehicles, respectively. Detection is based on a block-based variant of the AdaBoost detector with edge orientation histograms. The tracker uses a robust variant of the extended Lucas Kanade template matching algorithm. Results on day and night time sequences show detection on from a vehicle size of 20脳20 pixels, and tracking down to 10脳10 pixels, with a speed accuracy of 2.3%, for 95% of the vehicles.