HASTE: multi-Hypothesis Asynchronous Speeded-up Tracking of Events
OPEN ACCESS
Author / Producer
Date
2020-09
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Feature tracking using event cameras has experienced significant progress lately, with methods achieving comparable performance to feature trackers using traditional frame-based cameras, even outperforming them on certain challenging scenarios.
Most of the event-based trackers, however, still operate on intermediate, frame-like representations generated from accumulated events, on which traditional frame-based techniques can be adopted.
Attempting to harness the sparsity and asynchronicity of the event stream, other approaches have emerged to process each event individually, but they lack both in accuracy and efficiency in comparison to the event-based, frame-like alternatives.
Aiming to address this shortcoming of asynchronous approaches, in this paper, we propose an asynchronous patch-feature tracker that relies solely on events and processes each event individually as soon as it gets generated. We report significant improvements in tracking quality over the state of the art in publicly available datasets, while performing an order of magnitude more efficiently than similar asynchronous tracking approaches.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
Pages / Article No.
744
Publisher
ETH Zurich, Institute of Robotics and Intelligent Systems
Event
31st British Machine Vision Virtual Conference (BMVC 2020)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Event camera; Asynchronous processing; Aynchronous Vision; Computer Vision; Feature tracking; Event-based; Event-driven; SLAM; Visual Odometry
Organisational unit
09559 - Chli, Margarita (ehemalig) / Chli, Margarita (former)
Notes
Conference lecture will be held on September 10, 2020
Funding
644128 - Collaborative Aerial Robotic Workers (SBFI)
Related publications and datasets
Has part: https://github.com/ialzugaray/hasteHas part: https://youtu.be/6DZxIzrVLcI