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 extremely reliably in the most sophisticated scenarios but they
come at a price - modern event-based vision sensors have extremely low
resolution and produce a lot of noise. Moreover, the asynchronous nature of the
event stream calls for novel algorithms.
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, and not only the spatial component, at every moment of time. This
is done by approximating the 3D geometry of the event stream with a parametric
model; as a result, the algorithm is capable of producing the
motion-compensated event stream (effectively approximating egomotion), and
without using any form of external sensors in extremely low-light and noisy
conditions without any form of feature tracking or explicit optical flow
computation. 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.

本文提出了一种基于动态视觉传感器的异步相机的新颖跟踪算法，该算法可以在极低光和噪声条件下，无需任何外部传感器，且无需任何特征跟踪或显式光流计算即可检测和跟踪不同移动对象。