CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis
Published in IEEE International Workshop on Traffic and Street Surveillance for Safety and Security (AVSS), 2018
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%.
Recommended citation: @inproceedings{shah2018cadp, title={CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis}, author={Shah, Ankit and Lamare, Jean Baptiste and Nguyen-Anh, Tuan and Hauptmann, Alexander}, booktitle={2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)}, pages={1--9}, year={2018}, organization={IEEE} }
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