TY - GEN
T1 - Video Analytics for Detecting Motorcyclist Helmet Rule Violations
AU - Tsai, Chun Ming
AU - Hsieh, Jun Wei
AU - Chang, Ming Ching
AU - He, Guan Lin
AU - Chen, Ping Yang
AU - Chang, Wei Tsung
AU - Hsieh, Yi Kuan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violations of helmet rules among motorcyclists. Our method can handle highly challenging conditions for traditional methods, including occlusions, fast vehicle movement, shadows, large viewing angles, poor illumination and weather conditions. We adopt the widely used YOLOv7 object detector and develop a first baseline using YOLOv7-E6E. We further develop two improved versions, namely YOLOv7-CBAM and YOLOv7-SimAM that better address the challenges. Experiments are performed on the 2023 AI City Challenge Track 5 contest benchmark. Evaluation on the 100 test videos of the contest demonstrates the effectiveness of our approach. The baseline YOLOv7-E6E model trained with image size 1920 achieves 0.6112 mAP. The YOLOv7-CBAM achieves 0.6389 mAP, and YOLOv7-SimAM achieves 0.6422 mAP, where both are trained with image size 1280. These models rank sixth, fifth, and fourth on the public leaderboard, respectively, which outperforms over 36 global participating teams. The code for our models is available at: https://github.com/cmtsai2023/AICITY2023-Track5-DVHRM.
AB - The use of helmets is essential for motorcyclists' safety, but non-compliance with helmet rules remains a common issue. In this study, we extend the frontier of AI video analytic technologies for detecting violations of helmet rules among motorcyclists. Our method can handle highly challenging conditions for traditional methods, including occlusions, fast vehicle movement, shadows, large viewing angles, poor illumination and weather conditions. We adopt the widely used YOLOv7 object detector and develop a first baseline using YOLOv7-E6E. We further develop two improved versions, namely YOLOv7-CBAM and YOLOv7-SimAM that better address the challenges. Experiments are performed on the 2023 AI City Challenge Track 5 contest benchmark. Evaluation on the 100 test videos of the contest demonstrates the effectiveness of our approach. The baseline YOLOv7-E6E model trained with image size 1920 achieves 0.6112 mAP. The YOLOv7-CBAM achieves 0.6389 mAP, and YOLOv7-SimAM achieves 0.6422 mAP, where both are trained with image size 1280. These models rank sixth, fifth, and fourth on the public leaderboard, respectively, which outperforms over 36 global participating teams. The code for our models is available at: https://github.com/cmtsai2023/AICITY2023-Track5-DVHRM.
UR - http://www.scopus.com/inward/record.url?scp=85170820630&partnerID=8YFLogxK
U2 - 10.1109/CVPRW59228.2023.00566
DO - 10.1109/CVPRW59228.2023.00566
M3 - Conference contribution
AN - SCOPUS:85170820630
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 5366
EP - 5374
BT - Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
PB - IEEE Computer Society
T2 - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
Y2 - 18 June 2023 through 22 June 2023
ER -