  A large dataset of annotated traffic accidents is necessary to improve the
accuracy of traffic accident recognition using deep learning models.
Conventional traffic accident datasets provide annotations on traffic accidents
and other teacher labels, improving traffic accident recognition performance.
However, the labels annotated in conventional datasets need to be more
comprehensive to describe traffic accidents in detail. Therefore, we propose
V-TIDB, a large-scale traffic accident recognition dataset annotated with
various environmental information as multi-labels. Our proposed dataset aims to
improve the performance of traffic accident recognition by annotating ten types
of environmental information as teacher labels in addition to the presence or
absence of traffic accidents. V-TIDB is constructed by collecting many videos
from the Internet and annotating them with appropriate environmental
information. In our experiments, we compare the performance of traffic accident
recognition when only labels related to the presence or absence of traffic
accidents are trained and when environmental information is added as a
multi-label. In the second experiment, we compare the performance of the
training with only contact level, which represents the severity of the traffic
accident, and the performance with environmental information added as a
multi-label. The results showed that 6 out of 10 environmental information
labels improved the performance of recognizing the presence or absence of
traffic accidents. In the experiment on the degree of recognition of traffic
accidents, the performance of recognition of car wrecks and contacts was
improved for all environmental information. These experiments show that V-TIDB
can be used to learn traffic accident recognition models that take
environmental information into account in detail and can be used for
appropriate traffic accident analysis.
