Results 141 to 150 of about 322,190 (190)

PPO-GAT-Follow: Graph-Attention Reinforcement Learning for Robust Robot Person Following in Dense Crowds. [PDF]

Sensors (Basel)
Zhou X, Shi Y, Piao S, Gao C.
europepmc   +1 more source

Dual-view weakly-supervised learning for apple tree flower counting. [PDF]

Front Plant Sci
Vitousek M, Vrba J, Cejnek M, Jura J, Schankova K.   +4 more
europepmc   +1 more source

Low-annotation apple flower counting: A color-SAM enhanced and uncertainty-guided semi-supervised framework. [PDF]

Plant Phenomics
Wang Y, Guo X, Huang J, Chen C, Zhuge X, Chu W, Hao X.   +6 more
europepmc   +1 more source

A Two-Stage Coarse-to-Fine Framework for Sparse Crowd Density Prediction in Digital Twin-Based Safety Monitoring. [PDF]

Sensors (Basel)
Jeong Y, Kim S, Lee J, Lee D, Hwang T, Choi WG.   +5 more
europepmc   +1 more source

Robust crowd anomaly detection via hybrid ensemble learning for real-world surveillance. [PDF]

Sci Rep
Mabrouk D, Abdel-Fattah MA, Taha A.
europepmc   +1 more source

Robust object counting through distribution uncertainty matching and optimal transport. [PDF]

Sci Rep
Boughorbel S, Jarray F, Zegour R, Gilal NU, Thelaya KA, Agus M, Schneider J.   +6 more
europepmc   +1 more source

Crowd Gathering Detection Method Based on Multi-Scale Feature Fusion and Convolutional Attention. [PDF]

Sensors (Basel)
Yasen K, Zhou J, Zhou N, Qin K, Wang Z, Li Y.   +5 more
europepmc   +1 more source

Body Structure Aware Deep Crowd Counting

IEEE Transactions on Image Processing, 2018
Crowd counting is a challenging task, mainly due to the severe occlusions among dense crowds. This paper aims to take a broader view to address crowd counting from the perspective of semantic modeling.
Junwei Han, Rongrong Ji, Shenghua Gao
exaly   +2 more sources

Towards using count-level weak supervision for crowd counting [PDF]

Pattern Recognition, 2021
Most existing crowd counting methods require object location-level annotation, i.e., placing a dot at the center of an object. While being simpler than the bounding-box or pixel-level annotation, obtaining this annotation is still labor-intensive and time-consuming especially for images with highly crowded scenes.
Yinjie Lei, Pingping Zhang, Lingqiao Liu
exaly   +4 more sources

Crowd counting with crowd attention convolutional neural network

Neurocomputing, 2020
Accepted by ...
Wen Su, Jiwei Chen, Zengfu Wang
exaly   +3 more sources