A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes
The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nig...
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doaj-3453d3aa08694f0e9cc7be6e0931ee322020-11-25T00:38:32ZengMDPI AGSensors1424-82202013-12-011312164741649310.3390/s131216474s131216474A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic ScenesJianqiang Wang0Xiaoyan Sun1Junbin Guo2State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaSuzhou INVO Automotive Electronics Co., Ltd., Suzhou 215200, ChinaState Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaThe preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR) of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate.http://www.mdpi.com/1424-8220/13/12/16474advanced driver assistance systemnighttime vehicle detectionvehicle taillightspairingtrackingtime-series analysis modeladaptive thresholds |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jianqiang Wang Xiaoyan Sun Junbin Guo |
spellingShingle |
Jianqiang Wang Xiaoyan Sun Junbin Guo A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes Sensors advanced driver assistance system nighttime vehicle detection vehicle taillights pairing tracking time-series analysis model adaptive thresholds |
author_facet |
Jianqiang Wang Xiaoyan Sun Junbin Guo |
author_sort |
Jianqiang Wang |
title |
A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes |
title_short |
A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes |
title_full |
A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes |
title_fullStr |
A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes |
title_full_unstemmed |
A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes |
title_sort |
region tracking-based vehicle detection algorithm in nighttime traffic scenes |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2013-12-01 |
description |
The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR) of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate. |
topic |
advanced driver assistance system nighttime vehicle detection vehicle taillights pairing tracking time-series analysis model adaptive thresholds |
url |
http://www.mdpi.com/1424-8220/13/12/16474 |
work_keys_str_mv |
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_version_ |
1725297038921302016 |