TY  - JOUR
PY  - 2020//
TI  - A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition
JO  - Journal of advanced transportation
A1  - Wang, Chen
A1  - Dai, Yulu
A1  - Zhou, Wei
A1  - Geng, Yifei
SP  - e9194028
EP  - e9194028
VL  - 2020
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2020/9194028
ID  - ref1
ER  -