INCREMENTAL DETECTION AND TRACKING OF MOVING OBJECTS BY OPTICAL FLOW AND A CONTRARIO METHOD

This paper concerns moving objects detection and tracking based on the a contrario theory and on a Kalman filtering process. Only visual information is acquired from a B&W camera embedded on a mobile robot. KLT and a contrario theory are used to initially detect and cluster moving points. Then, each detected group of moving points is tracked as a moving object using Kalman Filter. The process detection-clustering-tracking is executed in an iterative way to deal with some challenges for real robot navigation. Furthermore, the area in which a moving obstacle is detected, is enlarged in the time until its real limits: clusters are fused with already detected objects considering similarities about their respective velocities and positions. Experimental results on real dynamic images acquired from a camera mounted on a moving robot, are presented and discussed.