A spatio-temporal clustering method using real-time motion analysis on event-based 3D vision

Abstract

This paper proposes a method for clustering asynchronous events generated upon scene activities by a dynamic 3D vision system. The inherent detection of moving objects offered by the dynamic stereo vision system comprising a pair of dynamic vision sensors allows event-based stereo vision in real-time and a 3D representation of moving objects. The clustering method exploits the sparse spatio-temporal representation of sensor's events for real-time detection and separation between moving objects. The method makes use of density and distance metrics for clustering asynchronous events generated by scene dynamics (changes in the scene). It has been evaluated on clustering the events of moving persons across the sensor field of view. Tests on real scenarios with more than 100 persons show that the resulting asynchronous events can be successfully clustered and the persons can be detected.