Results 91 to 100 of about 322,190 (190)
Crowd Counting and Individual Localization Using Pseudo Square Label
IEEE AccessRecent work in crowd counting focuses on counting over detected individuals rather than estimating the number of people in the image. However, existing crowd localization methods directly detect the head point or region of individuals, which may entail ...
Jihye Ryu, Kwangho Song
doaj +1 more source
Video-based crowd counting with information entropy
, 2015 As a key indicator of safety, the number of persons in pubic venues is quite important. However, most algorithms require a burdensome training, which is far away from practical application.
Zhou PP(周培培) +3 more
core
Spatiotemporal modeling for crowd counting in videos
, 2017 Crowd counting is an important task in computer vision. In this thesis, we focus on Region of Interest (ROI) crowd counting. ROI crowd counting can be formulated as a regression problem of learning a mapping from an image or a video frame to a crowd ...
Xiong, Feng
core
Calibrating Uncertainty for Semi-Supervised Crowd Counting
, 2023 Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set. The key is to use uncertainty to select reliable pseudo-labels.
Li, Chen +3 more
core
An Adaptive Multi-Scale Network Based on Depth Information for Crowd Counting. [PDF]
Sensors (Basel), 2023 Zhang P, Lei W, Zhao X, Dong L, Lin Z.
europepmc +1 more source
Foreground Segmentation-Based Density Grading Networks for Crowd Counting. [PDF]
Sensors (Basel), 2023 Liu Z, Zhou X, Zhou T, Chen Y.
europepmc +1 more source
MiCrowd: Vision-Based Deep Crowd Counting on MCU. [PDF]
Sensors (Basel), 2023 Son S +5 more
europepmc +1 more source
Exploring density rectification and domain adaption method for crowd counting. [PDF]
Neural Comput Appl, 2023 Peng S, Yin B, Yang Q, He Q, Wang L.
europepmc +1 more source
Locality-Constrained Spatial Transformer Network for Video Crowd Counting
, 2019 Compared with single image based crowd counting, video provides the spatial-temporal information of the crowd that would help improve the robustness of crowd counting.
Shenghua Gao +9 more
core +1 more source
Label Noise Robust Crowd Counting with Loss Filtering Factor
Applied Artificial IntelligenceCrowd counting, a crucial computer vision task, aims at estimating the number of individuals in various environments. Each person in crowd counting datasets is typically annotated by a point at the center of the head.
Zhengmeng Xu +3 more
doaj +1 more source