Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System
Joint Authors
Chen, Guang
Cao, Hu
Aafaque, Muhammad
Chen, Jieneng
Ye, Canbo
Röhrbein, Florian
Conradt, Jörg
Chen, Kai
Bing, Zhenshan
Liu, Xingbo
Hinz, Gereon
Knoll, Alois
Stechele, Walter
Source
Journal of Advanced Transportation
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-02
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras.
Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene at the time they occur.
This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources.
In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS.
The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge.
We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches.
Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras.
If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms.
We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions.
American Psychological Association (APA)
Chen, Guang& Cao, Hu& Aafaque, Muhammad& Chen, Jieneng& Ye, Canbo& Röhrbein, Florian…[et al.]. 2018. Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1181308
Modern Language Association (MLA)
Chen, Guang…[et al.]. Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System. Journal of Advanced Transportation No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1181308
American Medical Association (AMA)
Chen, Guang& Cao, Hu& Aafaque, Muhammad& Chen, Jieneng& Ye, Canbo& Röhrbein, Florian…[et al.]. Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1181308
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181308