A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition
In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibil...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/9194028 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566248161935360 |
|---|---|
| author | Chen Wang Yulu Dai Wei Zhou Yifei Geng |
| author_facet | Chen Wang Yulu Dai Wei Zhou Yifei Geng |
| author_sort | Chen Wang |
| collection | DOAJ |
| description | In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper. |
| format | Article |
| id | doaj-art-5c6f516eb37745199e019824dbcea8ca |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-5c6f516eb37745199e019824dbcea8ca2025-02-03T01:04:41ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/91940289194028A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility ConditionChen Wang0Yulu Dai1Wei Zhou2Yifei Geng3Intelligent Transportation Research Center, Southeast University, Nanjing 211189, ChinaIntelligent Transportation Research Center, Southeast University, Nanjing 211189, ChinaIntelligent Transportation Research Center, Southeast University, Nanjing 211189, ChinaSchool of Automation, Southeast University, Nanjing 211189, ChinaIn this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.http://dx.doi.org/10.1155/2020/9194028 |
| spellingShingle | Chen Wang Yulu Dai Wei Zhou Yifei Geng A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition Journal of Advanced Transportation |
| title | A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition |
| title_full | A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition |
| title_fullStr | A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition |
| title_full_unstemmed | A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition |
| title_short | A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition |
| title_sort | vision based video crash detection framework for mixed traffic flow environment considering low visibility condition |
| url | http://dx.doi.org/10.1155/2020/9194028 |
| work_keys_str_mv | AT chenwang avisionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition AT yuludai avisionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition AT weizhou avisionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition AT yifeigeng avisionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition AT chenwang visionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition AT yuludai visionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition AT weizhou visionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition AT yifeigeng visionbasedvideocrashdetectionframeworkformixedtrafficflowenvironmentconsideringlowvisibilitycondition |