Event-based pedestrian detection using dynamic vision sensors

Jixiang Wan, Ming Xia, Zunkai Huang, Li Tian, Xiaoying Zheng, Victor Chang, Yongxin Zhu*, Hui Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Pedestrian detection has attracted great research attention in video surveillance, traffic statistics, and especially in autonomous driving. To date, almost all pedestrian detection solutions are derived from conventional framed-based image sensors with limited reaction speed and high data redundancy. Dynamic vision sensor (DVS), which is inspired by biological retinas, efficiently captures the visual information with sparse, asynchronous events rather than dense, synchronous frames. It can eliminate redundant data transmission and avoid motion blur or data leakage in high-speed imaging applications. However, it is usually impractical to directly apply the event streams to conventional object detection algorithms. For this issue, we first propose a novel event-to-frame conversion method by integrating the inherent characteristics of events more efficiently. Moreover, we design an improved feature extraction network that can reuse intermediate features to further reduce the computational effort. We evaluate the performance of our proposed method on a custom dataset containing multiple real-world pedestrian scenes. The results indicate that our proposed method raised its pedestrian detection accuracy by about 5.6–10.8%, and its detection speed is nearly 20% faster than previously reported methods. Furthermore, it can achieve a processing speed of about 26 FPS and an AP of 87.43% when implanted on a single CPU so that it fully meets the requirement of real-time detection.

Original languageEnglish
Article number888
JournalElectronics (Switzerland)
Issue number8
Publication statusPublished - 8 Apr 2021

Bibliographical note

© 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://

Funding Information:
Funding: This research was funded by the National Key Research and Development Project (No. 2019YFC0117302), NSFC Youth Fund (No. 62004201), NSFC Youth Fund (No. 61704179), Shanghai Municipal Science and Technology Commission project (No. 19511131202), Pudong Economic and Technological Commission project (No. PKX2019-D02), Strategic Priority Research Program of Chinese Academy of Sciences (No. XDC02070700), Talents Project of Shanghai Advanced Research Institute Chinese Academy of Science (No. E052891ZZ1).


  • Autonomous driving
  • Dynamic vision sensor
  • Event data
  • Pedestrian detection


Dive into the research topics of 'Event-based pedestrian detection using dynamic vision sensors'. Together they form a unique fingerprint.

Cite this