Event-based Moving Object Detection and Tracking
Anton Mitrokhin, Cornelia Fermuller, Chethan Parameshwara, Yiannis, Aloimonos

TL;DR
This paper introduces a novel event stream representation and a parametric modeling approach for real-time object detection and tracking using asynchronous event-based vision sensors, effectively handling low resolution and noise.
Contribution
It presents a new method that leverages the dynamic component of event streams with a parametric model, enabling motion compensation and object tracking without external sensors or feature tracking.
Findings
Effective motion detection in noisy, low-light conditions
Real-time tracking of fast-moving objects
No reliance on feature tracking or optical flow
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 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…
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