BROWSE
Detail
Conference Paper
AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos
Cited 1 time in 
Share 
Share
- Authors
- Ji Sang Park, Ahyun Lee, Kang-Woo Lee, Sung Woong Shin, Soe Sandi Htun, Ji-Hyeong Han
- Issue Date
- 2022-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1480-1482
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper introduces a new modeling architecture to detect traffic anomalies using AI techniques. This study intends to reveal the effectiveness of merging extracted features which may be changed over predefined time period from dashcam video datasets. Relevant features are extracted by using a convolutional learning method and their temporal occurrence is modeled with a self-attention model. Segmented traffic accidents are classified into a couple of pre-defined groups indicating different traffic accident types. The analysis results show that the proposed modeling architecture is quite effective to identify traffic anomalies from dashcam video datasets. Additional issues for future analysis and implementations are discussed briefly as well.
- KSP Keywords
- AI techniques, Attention model, Dashcam video, Learning methods, Modeling architecture, Time period, Traffic accident, Traffic anomaly