Abstract
In this paper, we propose a novel approach for traffic
accident anticipation through (i) Adaptive Loss for Early
Anticipation (AdaLEA) and (ii) a large-scale self-annotated
incident database for anticipation. The proposed AdaLEA
allows a model to gradually learn an earlier anticipation
as training progresses. The loss function adaptively assigns
penalty weights depending on how early the model can anticipate a traffic accident at each epoch. Additionally, we
construct a Near-miss Incident DataBase for anticipation.
This database contains an enormous number of traffic nearmiss incident videos and annotations for detail evaluation
of two tasks, risk anticipation and risk-factor anticipation.
In our experimental results, we found our proposal achieved
the highest scores for risk anticipation (+6.6% better on
mean average precision (mAP) and 2.36 sec earlier than
previous work on the average time-to-collision (ATTC)) and
risk-factor anticipation (+4.3% better on mAP and 0.70 sec
earlier than previous work on ATTC).