Event Blob Tracking: An Asynchronous Real-Time Algorithm
- URL: http://arxiv.org/abs/2307.10593v1
- Date: Thu, 20 Jul 2023 05:15:03 GMT
- Title: Event Blob Tracking: An Asynchronous Real-Time Algorithm
- Authors: Ziwei Wang, Timothy Molloy, Pieter van Goor, Robert Mahony
- Abstract summary: We propose a novel algorithm for tracking event blobs using raw events asynchronously in real time.
Our algorithm achieves highly accurate tracking and event blob shape estimation even under challenging lighting conditions.
The filter output can be used to derive secondary information such as time-to-contact or range estimation.
- Score: 14.312736839139417
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Event-based cameras have become increasingly popular for tracking fast-moving
objects due to their high temporal resolution, low latency, and high dynamic
range. In this paper, we propose a novel algorithm for tracking event blobs
using raw events asynchronously in real time. We introduce the concept of an
event blob as a spatio-temporal likelihood of event occurrence where the
conditional spatial likelihood is blob-like. Many real-world objects generate
event blob data, for example, flickering LEDs such as car headlights or any
small foreground object moving against a static or slowly varying background.
The proposed algorithm uses a nearest neighbour classifier with a dynamic
threshold criteria for data association coupled with a Kalman filter to track
the event blob state. Our algorithm achieves highly accurate tracking and event
blob shape estimation even under challenging lighting conditions and high-speed
motions. The microsecond time resolution achieved means that the filter output
can be used to derive secondary information such as time-to-contact or range
estimation, that will enable applications to real-world problems such as
collision avoidance in autonomous driving.
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