AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos

Ji Sang Park; Ahyun Lee; Kang Woo Lee; Sung Woong Shin; Soe Sandi Htun; Ji Hyeong Han

doi:10.1109/ICTC55196.2022.9952473

AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos

Ji Sang Park
, Ahyun Lee
, Kang Woo Lee
, Sung Woong Shin
, Soe Sandi Htun
, Ji Hyeong Han

Dept. of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publication	Accelerating Digital Transformation with ICT Innovation
Publisher	IEEE Computer Society
Pages	1480-1482
Number of pages	3
ISBN (Electronic)	9781665499392
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952473
State	Published - 2022
Event	13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of Duration: 19 Oct 2022 → 21 Oct 2022

Publication series

Name	International Conference on ICT Convergence
Volume	2022-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	19/10/22 → 21/10/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

action recognition
machine learning
object detection
traffic anomaly

Access to Document

10.1109/ICTC55196.2022.9952473

Cite this

Park, J. S., Lee, A., Lee, K. W., Shin, S. W., Htun, S. S., & Han, J. H. (2022). AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation (pp. 1480-1482). (International Conference on ICT Convergence; Vol. 2022-October). IEEE Computer Society. https://doi.org/10.1109/ICTC55196.2022.9952473

Park, Ji Sang ; Lee, Ahyun ; Lee, Kang Woo et al. / AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos. ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. pp. 1480-1482 (International Conference on ICT Convergence).

@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",

}

Park, JS, Lee, A, Lee, KW, Shin, SW, Htun, SS & Han, JH 2022, AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos. in ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. International Conference on ICT Convergence, vol. 2022-October, IEEE Computer Society, pp. 1480-1482, 13th International Conference on Information and Communication Technology Convergence, ICTC 2022, Jeju Island, Korea, Republic of, 19/10/22. https://doi.org/10.1109/ICTC55196.2022.9952473

AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos. / Park, Ji Sang; Lee, Ahyun; Lee, Kang Woo et al.
ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society, 2022. p. 1480-1482 (International Conference on ICT Convergence; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos

AU - Park, Ji Sang

AU - Lee, Ahyun

AU - Lee, Kang Woo

AU - Shin, Sung Woong

AU - Htun, Soe Sandi

AU - Han, Ji Hyeong

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - action recognition

KW - machine learning

KW - object detection

KW - traffic anomaly

UR - https://www.scopus.com/pages/publications/85143256236

U2 - 10.1109/ICTC55196.2022.9952473

DO - 10.1109/ICTC55196.2022.9952473

M3 - Conference contribution

AN - SCOPUS:85143256236

T3 - International Conference on ICT Convergence

SP - 1480

EP - 1482

BT - ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence

PB - IEEE Computer Society

T2 - 13th International Conference on Information and Communication Technology Convergence, ICTC 2022

Y2 - 19 October 2022 through 21 October 2022

ER -

Park JS, Lee A, Lee KW, Shin SW, Htun SS, Han JH. AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos. In ICTC 2022 - 13th International Conference on Information and Communication Technology Convergence: Accelerating Digital Transformation with ICT Innovation. IEEE Computer Society. 2022. p. 1480-1482. (International Conference on ICT Convergence). doi: 10.1109/ICTC55196.2022.9952473

AI-Based Modeling Architecture to Detect Traffic Anomalies from Dashcam Videos

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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