Results 101 to 110 of about 322,190 (190)

Offset-decoupled deformable convolution for efficient crowd counting. [PDF]

Sci Rep, 2022
Zhong X, Qin J, Guo M, Zuo W, Lu W.
europepmc   +1 more source

Deep learning in crowd counting: A survey

CAAI Transactions on Intelligence Technology
Counting high‐density objects quickly and accurately is a popular area of research. Crowd counting has significant social and economic value and is a major focus in artificial intelligence.
Lijia Deng, Qinghua Zhou, Shuihua Wang, Juan Manuel Górriz, Yudong Zhang   +4 more
doaj   +1 more source

A Realistic and New Approach for Crowd Density, Quantity, Types, and Anomaly Detection Using Effective Multi-Task Deep Learning Model

IEEE Access
Quantitative, type, and anomaly information in crowd videos is critical for smart city and campus applications. Existing approaches generally focus on high-performance counting or anomaly detection within the scope of crowd analysis.
Turan Goktug Altundogan, Selen Gurbuz, Mehmet Karakose   +2 more
doaj   +1 more source

Application of improved transformer based on weakly supervised in crowd localization and crowd counting. [PDF]

Sci Rep, 2023
Gao H, Zhao W, Zhang D, Deng M.
europepmc   +1 more source

A Crowd Counting and Localization Network Based on Adaptive Feature Fusion and Multi-Scale Global Attention Up Sampling

IEEE Access
Crowd counting is an important research topic in the fields of computer vision and image processing, with monitoring and management of crowded scenes becoming an increasingly prominent issue.
Min Wang, Li Huang, Jingke Yan, Jin Huang, Tao Yang   +4 more
doaj   +1 more source

Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review. [PDF]

Sensors (Basel), 2022
Hassen KBA, Machado JJM, Tavares JMRS.
europepmc   +1 more source

Texture-based Homogeneity Analysis for Crowd Scene Modelling and Abnormality Detection [PDF]

Video-based crowd behaviour analysis techniques aim at tackling challenging problems such as detecting abnormal crowd behaviours and tracking specific individuals from complex real life scenes.
Xu, Zhijie, Wang, Jing
core   +1 more source

Multimodal Crowd Counting with Pix2Pix GANs

Most state-of-the-art crowd counting methods use color (RGB) images to learn the density map of the crowd. However, these methods often struggle to achieve higher accuracy in densely crowded scenes with poor illumination.
Menouar, Hamid, Hamila, Ridha, Khan, Muhammad Asif   +2 more
core   +2 more sources

HRANet: Hierarchical region-aware network for crowd counting. [PDF]

Appl Intell (Dordr), 2022
Xie J, Gu L, Li Z, Lyu L.
europepmc   +1 more source

Cascaded parallel crowd counting network with multi-resolution collaborative representation. [PDF]

Appl Intell (Dordr), 2023
Lyu L, Han R, Chen Z.
europepmc   +1 more source