Evaluation of Object Tracking in Traffic Scenes

Abstract

Trajectories extracted from an object tracker are a valuable source of information to derive describing parameters for traffic situation analysis. In order to ensure the quality of these parameters, the tracker's output has to be evaluated. This is a challenging task, since the choice of the metrics and the choice of the matching --- of ground truth tracks and the output of the tracker --- influences the results considerably. We present the evaluation of a Kalman-filter based tracker using ground truth data of traffic scenes. The Kalman-Filter is object space based and uses observations obtained from multiple camera views. In the evaluation, a two level approach is employed. The first level assesses the ability to correctly locate the objects. The second one evaluates the consistent identification of objects throughout the scene. Matching of tracks is done using a spatial overlap measure. Subsequently, several metrics are applied, whose properties are outlined. The results for simulated and real traffic data are presented. Finally, the implications of aggregation and normalization of these metrics in order to estimate the algorithm's performance are discussed.