TY - GEN
T1 - The 8th AI City Challenge
AU - Wang, Shuo
AU - Anastasiu, David C.
AU - Tang, Zheng
AU - Chang, Ming Ching
AU - Yao, Yue
AU - Zheng, Liang
AU - Rahman, Mohammed Shaiqur
AU - Arya, Meenakshi S.
AU - Sharma, Anuj
AU - Chakraborty, Pranamesh
AU - Prajapati, Sanjita
AU - Kong, Quan
AU - Kobori, Norimasa
AU - Gochoo, Munkhjargal
AU - Otgonbold, Munkh Erdene
AU - Alnajjar, Fady
AU - Batnasan, Ganzorig
AU - Chen, Ping Yang
AU - Hsieh, Jun Wei
AU - Wu, Xunlei
AU - Pusegaonkar, Sameer Satish
AU - Wang, Yizhou
AU - Biswas, Sujit
AU - Chellappa, Rama
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements.
AB - The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements.
UR - http://www.scopus.com/inward/record.url?scp=85204901943&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204901943&partnerID=8YFLogxK
U2 - 10.1109/CVPRW63382.2024.00722
DO - 10.1109/CVPRW63382.2024.00722
M3 - Conference contribution
AN - SCOPUS:85204901943
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 7261
EP - 7272
BT - Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
PB - IEEE Computer Society
T2 - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
Y2 - 16 June 2024 through 22 June 2024
ER -