Visual object tracking under challenging conditions of motion and light can be hindered by the capabilities of conventional cameras, prone to producing images with motion blur. Event cameras are novel sensors suited to robustly perform vision tasks under these conditions. However, due to the nature of their output, applying them to object detection and tracking is non-trivial. In this work, we propose a framework to take advantage of both event cameras and off-the-shelf deep learning for object tracking. We show that reconstructing event data into intensity frames improves the tracking performance in conditions under which conventional cameras fail to provide acceptable results.
Event-Based Visual Tracking in Dynamic Environments
Lect. Notes in Networks, Syst.
Iberian Robotics conference ; 2022 ; Zaragoza, Spain November 23, 2022 - November 25, 2022
19.11.2022
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Real-Time Event-Based Tracking and Detection for Maritime Environments
DataCite | 2024
|Dynamic geodesic snakes for visual tracking
IEEE | 2004
|Dynamic Geodesic Snakes for Visual Tracking
British Library Conference Proceedings | 2004
|