A multi-cue approach for stereo-based object confidence estimation

In this contribution we present an approach to compute object confidences for stereo-vision-based object tracking schemes. Meaningful object confidences help to reduce false alarm rates of safety systems and improve the downstream system performance for modules such as sensor fusion and situation analysis. Several cues from stereo vision and from the tracking process are fused in a Bayesian manner. An evaluation on a 38,000 frames urban drive shows the effectiveness of the approach compared to the same object tracking scheme with simple heuristics for the object confidence. Within the evaluation, also the relevance of occurring phantoms is considered by computing the collision risk. The proposed confidence measures reduce the number of predicted imminent collisions from 86 to 0 maintaining almost the same system availability.