Meeting in the Middle: A top-down and bottom-up approach to detect pedestrians

This paper proposes a generic approach combining a bottom-up (low-level) visual detector with a top-down (high-level) fuzzy first-order logic (FOL) reasoning framework in order to detect pedestrians from a moving vehicle. Detections from the low-level visual corner based detector are fed into the logical reasoning framework as logical facts. A set of FOL clauses utilising fuzzy predicates with piecewise linear continuous membership functions associates a fuzzy confidence (a degree-of-truth) to each detector input. Detections associated with lower confidence functions are deemed as false positives and blanked out, thus adding top-down constraints based on global logical consistency of detections. We employ a state of the art visual detector on a challenging pedestrian detection dataset, and demonstrate an increase in detection performance when used in a framework that combines bottom-up detections with (fuzzy FOL-based) top-down constraints.