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

翻译：本文介绍了交通事故分析的新数据集。我们的目标是解决道路交通安全自动时空说明研究缺乏公共数据的问题。通过分析拟议的数据集,我们观察到,由于天体大小和场景的复杂性,在我们的数据集中行人类别的物体探测显著退化。为此,我们提议利用环境采矿和增强环境采矿(ACM),将背景信息纳入常规更快的R-CNN,以补充小行人探测的准确性。我们的实验表明,物体探测精确度有相当大的改进:CM+8.51%,ACM+6.20%。最后,我们用更快的R-CN和事故LSTM结构展示了我们数据集中事故预报的性能。我们实现了平均精确度为47.25%的时间到异常度平均1.684秒。我们的网页是 https://goo.gl/cqK2wE。我们的文件网页是 https://goo.gl/cqK2wE。