A Novel Dataset for CCTV Traffic Camera based Accident Analysis
We present a novel dataset for traffic accidents analysis. Our aim is to resolve the lack of public data for research about automatic spatio-temporal annotations for traffic safety in the roads. Our Car Accident Detection and Prediction (CADP) dataset consists of 1,416 video segments collected from YouTube, with 205 video segments have full spatio-temporal annotations. Through analysis of CADP 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 the Augmented Context Mining (ACM) into the Faster R-CNN detector to complement the accuracy for small pedestrian detection. Our experiments indicated a considerable improvement in object detection accuracy +8.51% for CM and +6.20% for ACM. For person (pedestrian) category, we observed significant improvements: +46.45% for CM and +45.22% for ACM, compared to Faster R-CNN. Finally, we demonstrate the performance of accident forecasting in our dataset using our Imporved Faster R-CNN and the Accident LSTM architectures. We achieved an average 1.684 seconds in terms of Time-To-Accident measure with highest Average Precision is 47.25 %. We expect our dataset can serve as the starting point of a new research direction, which can grow incrementally in coming years.