eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-
- URL: http://arxiv.org/abs/2207.09108v2
- Date: Wed, 20 Jul 2022 05:40:43 GMT
- Title: eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-
- Authors: Sumin Hu, Yeeun Kim, Hyungtae Lim, Alex Junho Lee, Hyun Myung
- Abstract summary: Event Clustering-based Detection Tracking (eCDT)
Our method 30% better feature tracking ages compared with the state-of-the-art approach.
- Score: 4.094848360328624
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Contrary to other standard cameras, event cameras interpret the world in an
entirely different manner; as a collection of asynchronous events. Despite
event camera's unique data output, many event feature detection and tracking
algorithms have shown significant progress by making detours to frame-based
data representations. This paper questions the need to do so and proposes a
novel event data-friendly method that achieve simultaneous feature detection
and tracking, called event Clustering-based Detection and Tracking (eCDT). Our
method employs a novel clustering method, named as k-NN Classifier-based
Spatial Clustering and Applications with Noise (KCSCAN), to cluster adjacent
polarity events to retrieve event trajectories.With the aid of a Head and Tail
Descriptor Matching process, event clusters that reappear in a different
polarity are continually tracked, elongating the feature tracks. Thanks to our
clustering approach in spatio-temporal space, our method automatically solves
feature detection and feature tracking simultaneously. Also, eCDT can extract
feature tracks at any frequency with an adjustable time window, which does not
corrupt the high temporal resolution of the original event data. Our method
achieves 30% better feature tracking ages compared with the state-of-the-art
approach while also having a low error approximately equal to it.
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