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A large dataset of annotated traffic accidents is necessary to improve theaccuracy of traffic accident recognition using deep learning models.Conventional traffic accident datasets provide annotations on traffic accidentsand other teacher labels, improving traffic accident recognition performance.However, the labels annotated in conventional datasets need to be morecomprehensive to describe traffic accidents in detail. Therefore, we proposeV-TIDB, a large-scale traffic accident recognition dataset annotated withvarious environmental information as multi-labels. Our proposed dataset aims toimprove the performance of traffic accident recognition by annotating ten typesof environmental information as teacher labels in addition to the presence orabsence of traffic accidents. V-TIDB is constructed by collecting many videosfrom the Internet and annotating them with appropriate environmentalinformation. In our experiments, we compare the performance of traffic accidentrecognition when only labels related to the presence or absence of trafficaccidents are trained and when environmental information is added as amulti-label. In the second experiment, we compare the performance of thetraining with only contact level, which represents the severity of the trafficaccident, and the performance with environmental information added as amulti-label. The results showed that 6 out of 10 environmental informationlabels improved the performance of recognizing the presence or absence oftraffic accidents. In the experiment on the degree of recognition of trafficaccidents, the performance of recognition of car wrecks and contacts wasimproved for all environmental information. These experiments show that V-TIDBcan be used to learn traffic accident recognition models that takeenvironmental information into account in detail and can be used forappropriate traffic accident analysis.

Traffic Incident Database with Multiple Labels Including Various Perspective Environmental Information