Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System
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...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/4815383 |
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| _version_ | 1832548405816066048 |
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| author | Guang Chen Hu Cao Muhammad Aafaque Jieneng Chen Canbo Ye Florian Röhrbein Jörg Conradt Kai Chen Zhenshan Bing Xingbo Liu Gereon Hinz Walter Stechele Alois Knoll |
| author_facet | Guang Chen Hu Cao Muhammad Aafaque Jieneng Chen Canbo Ye Florian Röhrbein Jörg Conradt Kai Chen Zhenshan Bing Xingbo Liu Gereon Hinz Walter Stechele Alois Knoll |
| author_sort | Guang Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-80077ea73881438ba1caf282b4011c68 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-80077ea73881438ba1caf282b4011c682025-02-03T06:14:12ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/48153834815383Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation SystemGuang Chen0Hu Cao1Muhammad Aafaque2Jieneng Chen3Canbo Ye4Florian Röhrbein5Jörg Conradt6Kai Chen7Zhenshan Bing8Xingbo Liu9Gereon Hinz10Walter Stechele11Alois Knoll12College of Automotive Engineering, Tongji University, ChinaState Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, ChinaRobotics and Embedded Systems, Technische Universität München, GermanyCollege of Electronics and Information Engineering, Tongji University, ChinaCollege of Automotive Engineering, Tongji University, ChinaRobotics and Embedded Systems, Technische Universität München, GermanyDepartment of Computational Science and Technology, KTH Royal Institute of Technology, SwedenCollege of Automotive Engineering, Tongji University, ChinaRobotics and Embedded Systems, Technische Universität München, GermanyCollege of Automotive Engineering, Tongji University, ChinaRobotics and Embedded Systems, Technische Universität München, GermanyIntegrated Systems, Technische Universität München, GermanyRobotics and Embedded Systems, Technische Universität München, GermanyNeuromorphic 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.http://dx.doi.org/10.1155/2018/4815383 |
| spellingShingle | Guang Chen Hu Cao Muhammad Aafaque Jieneng Chen Canbo Ye Florian Röhrbein Jörg Conradt Kai Chen Zhenshan Bing Xingbo Liu Gereon Hinz Walter Stechele Alois Knoll Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System Journal of Advanced Transportation |
| title | Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System |
| title_full | Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System |
| title_fullStr | Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System |
| title_full_unstemmed | Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System |
| title_short | Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System |
| title_sort | neuromorphic vision based multivehicle detection and tracking for intelligent transportation system |
| url | http://dx.doi.org/10.1155/2018/4815383 |
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