TY  - JOUR
PY  - 2022//
TI  - Spatio-temporal feature encoding for traffic accident detection in VANET environment
JO  - IEEE transactions on intelligent transportation systems
A1  - Zhou, Zhili
A1  - Dong, Xiaohua
A1  - Li, Zhetao
A1  - Yu, Keping
A1  - Ding, Chun
A1  - Yang, Yimin
SP  - 19772
EP  - 19781
VL  - 23
IS  - 10
N2  - In the Vehicular Ad hoc Networks (VANET) environment, recognizing traffic accident events in the driving videos captured by vehicle-mounted cameras is an essential task. Generally, traffic accidents have a short duration in driving videos, and the backgrounds of driving videos are dynamic and complex. These make traffic accident detection quite challenging. To effectively and efficiently detect accidents from the driving videos, we propose an accident detection approach based on spatio-temporal feature encoding with a multilayer neural network. Specifically, the multilayer neural network is used to encode the temporal features of video for clustering the video frames. From the obtained frame clusters, we detect the border frames as the potential accident frames. Then, we capture and encode the spatial relationships of the objects detected from these potential accident frames to confirm whether these frames are accident frames. The extensive experiments demonstrate that the proposed approach achieves promising detection accuracy and efficiency for traffic accident detection, and meets the real-time detection requirement in the VANET environment.<p />  <p>Language: en</p>
LA  - en
SN  - 1524-9050
UR  - http://dx.doi.org/10.1109/TITS.2022.3147826
ID  - ref1
ER  -