research-article

E-detector: Asynchronous Spatio-temporal for Event-based Object Detection in Intelligent Transportation System

Authors:
Shixiong Zhang

School of Computer Science and Engineering, Macau University of Science and Technology, China

School of Computer Science and Engineering, Macau University of Science and Technology, China

0000-0002-3816-839X
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,
Wenmin Wang

School of Computer Science and Engineering, Macau University of Science and Technology, China

School of Computer Science and Engineering, Macau University of Science and Technology, China

0000-0003-2664-4413
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,
Honglei Li

School of Computer Science and Engineering, Macau University of Science and Technology, China

School of Computer Science and Engineering, Macau University of Science and Technology, China

0000-0003-0777-0799
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,
Shenyong Zhang

School of Computer Science and Engineering, Macau University of Science and Technology, China

School of Computer Science and Engineering, Macau University of Science and Technology, China

0000-0001-5137-4828
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ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20 Issue 2Article No.: 36pp 1–20https://doi.org/10.1145/3584361

Published:27 September 2023Publication History

ACM Transactions on Multimedia Computing, Communications, and Applications

Abstract

In intelligent transportation systems, various sensors, including radar and conventional frame cameras, are used to improve system robustness in various challenging scenarios. An event camera is a novel bio-inspired sensor that has attracted the interest of several researchers. It provides a form of neuromorphic vision to capture motion information asynchronously at high speeds. Thus, it possesses advantages for intelligent transportation systems that conventional frame cameras cannot match, such as high temporal resolution, high dynamic range, as well as sparse and minimal motion blur. Therefore, this study proposes an E-detector based on event cameras that asynchronously detect moving objects. The main innovation of our framework is that the spatiotemporal domain of the event camera can be adjusted according to different velocities and scenarios. It overcomes the inherent challenges that traditional cameras face when detecting moving objects in complex environments, such as high speed, complex lighting, and motion blur. Moreover, our approach adopts filter models and transfer learning to improve the performance of event-based object detection. Experiments have shown that our method can detect high-speed moving objects better than conventional cameras using state-of-the-art detection algorithms. Thus, our proposed approach is extremely competitive and extensible, as it can be extended to other scenarios concerning high-speed moving objects. The study findings are expected to unlock the potential of event cameras in intelligent transportation system applications.

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Index Terms

E-detector: Asynchronous Spatio-temporal for Event-based Object Detection in Intelligent Transportation System
1. Computing methodologies
  1. Machine learning

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Published in
ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 2
February 2024
548 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3613570
Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada
Issue’s Table of Contents
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Publication History
- Published: 27 September 2023
- Online AM: 17 February 2023
- Accepted: 10 February 2023
- Revised: 7 February 2023
- Received: 29 April 2022
Published in tomm Volume 20, Issue 2

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Author Tags
Bio-inspired sensor
event-based vision
adaptable framework
moving object detection
Qualifiers
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E-detector: Asynchronous Spatio-temporal for Event-based Object Detection in Intelligent Transportation System

ACM Transactions on Multimedia Computing, Communications, and Applications

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