  This paper presents a novel dataset for traffic accidents analysis. Our goal
is to resolve the lack of public data for research about automatic
spatio-temporal annotations for traffic safety in the roads. Through the
analysis of the proposed dataset, we observed a significant degradation of
object detection in pedestrian category in our dataset, due to the object sizes
and complexity of the scenes. To this end, we propose to integrate contextual
information into conventional Faster R-CNN using Context Mining (CM) and
Augmented Context Mining (ACM) to complement the accuracy for small pedestrian
detection. Our experiments indicate a considerable improvement in object
detection accuracy: +8.51% for CM and +6.20% for ACM. Finally, we demonstrate
the performance of accident forecasting in our dataset using Faster R-CNN and
an Accident LSTM architecture. We achieved an average of 1.684 seconds in terms
of Time-To-Accident measure with an Average Precision of 47.25%. Our Webpage
for the paper is https://goo.gl/cqK2wE
