@inproceedings{999969ff5a484628a2ed3f127ff3b801,
title = "AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos",
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.",
keywords = "action recognition, machine learning, object detection, traffic anomaly",
author = "Park, \{Ji Sang\} and Ahyun Lee and Lee, \{Kang Woo\} and Shin, \{Sung Woong\} and Htun, \{Soe Sandi\} and Han, \{Ji Hyeong\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Technology Convergence, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",
year = "2022",
doi = "10.1109/ICTC55196.2022.9952473",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "1480--1482",
booktitle = "ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence",
}
