Event-Based Moving Object Detection and Tracking

Abstract

Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity to light and low latency. These properties provide the grounds to estimate motion efficiently and reliably in the most sophisticated scenarios, but these advantages come at a price - modern event-based vision sensors have extremely low resolution, produce a lot of noise and require the development of novel algorithms to handle the asynchronous event stream. This paper presents a new, efficient approach to object tracking with asynchronous cameras. We present a novel event stream representation which enables us to utilize information about the dynamic (temporal)component of the event stream. The 3D geometry of the event stream is approximated with a parametric model to motion-compensate for the camera (without feature tracking or explicit optical flow computation), and then moving objects that don't conform to the model are detected in an iterative process. We demonstrate our framework on the task of independent motion detection and tracking, where we use the temporal model inconsistencies to locate differently moving objects in challenging situations of very fast motion.