MM-trafficEvent: An Interactive Incident Retrieval System for First-view Travel-log Data
Minh-Son Dao, Dinh-Duy Pham, Manh-Phu Nguyen, Thanh-Binh Nguyen, Koji Zettu · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Dashcam video has become popular recently due to the safety of both individuals and communities. While an individual can have undeniable evidence for legal and insurance, communities can benefit from sharing these dashcam videos for further traffic education and criminal investigation. Moreover, relying on recent computer vision and AI development, few companies have launched the so-called AI dashcam that can alert drivers to near-risk accidents (e.g.., following distance detection, forward collision warning), forwarding to improving driver’s safety. However, even though dashcam videos create a driver’s travel log (i.e., traveling diary), little research focuses on creating a valuable and friendly tool to find any incident or event with few described sketches by users. Besides, most incident detection models have been built using a traditional supervised learning approach (i.e., collecting and labeling data for a new incident class). That prevents the quick and customized development of a new incident class. Inspired from these observations, we introduce an interactive incident detection and retrieval system for first-view travel-log data, namely MM-trafficEvent, that can (1) online defined-incident detection, (2) offline fine-grained incident retrieval for both defined and undefined incidents, (3) offline automatically new incident class creating using user’s queries. Moreover, the system gives promising results when being evaluated on several public datasets.