REFINING CORRECTNESS OF VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE SEQUENCES BY MEANS OF VELOCITY AND TRAJECTORY EVALUATION
Dominik Lenhart, Stefan Hinz · 2009
Derivation of statistical traffic data is highly dependent on the balance of detection and false alarm rates. In case false alarms have not been eliminated in the initial detection phase, they are often subsequently tracked, though, resulting in trajectories that do not match the true traffic situation. This finally leads to derivation of erroneous traffic parameters within the individual road segments. In this paper, a method is described how to eliminate false alarms by evaluating the trajectories and velocities of a tracking procedure. Basically, two types of false alarms are considered which bias the statistics of traffic data: The first type deals with redundant detections that lead to multiple trajectories biasing the statistics. The second type comprises false alarms that belong to the static background inducing zero-velocity into the statistics. We show that the presented procedure is able to increase the total correctness of detection and tracking from 65 % up to 95 % which allows a much more precise calculation of traffic flow parameters. 1. TRAFFIC MONITORING The task of collecting wide area traffic parameters plays important role in today’s traffic management. Aerial images offer a complement source to common measurement systems like induction loops and stationary video cameras. Besides giving a visual overview, image sequences which cover large areas can deliver a time snapshot of a spatially fully covered