TY - GEN
T1 - Density-Based Flow Mask Integration via Deformable Convolution for Video People Flux Estimation
AU - Wan, Chang Lin
AU - Huang, Feng Kai
AU - Shuai, Hong Han
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/3
Y1 - 2024/1/3
N2 - Crowd counting is currently applied in many areas, such as transportation hubs and streets. However, most of the research still focuses on counting the number of people in a single image, and there is little research on solving the problem of calculating the number of non-repeated people in a video segment. Currently, multiple object tracking is mainly relied upon for video counting, but this method is not suitable for situations where the crowd density is too high. Therefore, we propose a Flow Mask Integration Deformable Convolution network (FMDC) combined with Inter-Frame Head Contrastive Learning (IFHC) to predict the situation of people entering and exiting the screen in a density-based manner. We verify that our proposed method is highly effective in densely populated situations and diverse scenes, and the experimental results show that our proposed method surpasses existing methods.
AB - Crowd counting is currently applied in many areas, such as transportation hubs and streets. However, most of the research still focuses on counting the number of people in a single image, and there is little research on solving the problem of calculating the number of non-repeated people in a video segment. Currently, multiple object tracking is mainly relied upon for video counting, but this method is not suitable for situations where the crowd density is too high. Therefore, we propose a Flow Mask Integration Deformable Convolution network (FMDC) combined with Inter-Frame Head Contrastive Learning (IFHC) to predict the situation of people entering and exiting the screen in a density-based manner. We verify that our proposed method is highly effective in densely populated situations and diverse scenes, and the experimental results show that our proposed method surpasses existing methods.
KW - Algorithms
KW - Algorithms
KW - Algorithms
KW - Image recognition and understanding
KW - Machine learning architectures
KW - Video recognition and understanding
KW - and algorithms
KW - formulations
UR - http://www.scopus.com/inward/record.url?scp=85191941442&partnerID=8YFLogxK
U2 - 10.1109/WACV57701.2024.00644
DO - 10.1109/WACV57701.2024.00644
M3 - Conference contribution
AN - SCOPUS:85191941442
T3 - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
SP - 6559
EP - 6568
BT - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Y2 - 4 January 2024 through 8 January 2024
ER -