Event-based Visual Tracking in Dynamic Environments

TL;DR

This paper presents a framework combining event cameras and deep learning to improve object tracking in challenging conditions where traditional cameras struggle, demonstrating enhanced performance through event data reconstruction.

Contribution

The work introduces a novel approach that leverages event camera data and deep learning, with a reconstruction method to enhance tracking in difficult environments.

Findings

Reconstructed event data improves tracking accuracy.

Event cameras outperform conventional cameras in motion and lighting challenges.

Deep learning integration enhances tracking robustness.

Abstract

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.