A Real-time Pedestrian Classification Method for Event-based Dynamic Stereo Vision

This paper proposes a real-time implementation of a
clustering and classification method using asynchronous
events generated upon scene activities by an event-based
dynamic stereo 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 and
classification method exploit 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. The method has been implemented on the Blackfin
BF537 from analog device and tested on real scenarios
with more than 100 persons. The results show that the
resulting asynchronous events can be successfully
clustered in real-time and that the classification rate of
pedestrians is successful in more than 92% of the cases.