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Crowd Counting

Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.

Source: Deep Density-aware Count Regressor

Papers

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Showing 251–300 of 371 papers

TitleStatusHype
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds—0
Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localization—0
Content-aware Density Map for Crowd Counting and Density Estimation—0
Count-guided Weakly Supervised Localization Based on Density Map—0
Counting and Segmenting Sorghum Heads—0
Counting Crowds in Bad Weather—0
Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches—0
Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects—0
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation—0
Crossing the Line: Crowd Counting by Integer Programming with Local Features—0
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks—0
Cross-View Cross-Scene Multi-View Crowd Counting—0
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network—0
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks—0
Crowd Counting and Individual Localization Using Pseudo Square Label—0
Crowd Counting by Adapting Convolutional Neural Networks with Side Information—0
Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid—0
Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification—0
Crowd Counting Considering Network Flow Constraints in Videos—0
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs—0
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training—0
Crowd Counting Through Walls Using WiFi—0
Crowd Counting using Deep Recurrent Spatial-Aware Network—0
Crowd Counting Using Scale-Aware Attention Networks—0
Crowd Counting via Hierarchical Scale Recalibration Network—0
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps—0
Crowd counting with crowd attention convolutional neural network—0
Crowd Counting with Decomposed Uncertainty—0
Crowd Counting with Deep Structured Scale Integration Network—0
Crowd Counting with Density Adaption Networks—0
Crowd Counting with Online Knowledge Learning—0
Crowd counting with segmentation attention convolutional neural network—0
Crowd Counting with Sparse Annotation—0
Crowd Density Estimation using Imperfect Labels—0
Crowd Density Estimation using Novel Feature Descriptor—0
CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models—0
CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability—0
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP—0
Crowd Scene Analysis using Deep Learning Techniques—0
Crowd Transformer Network—0
Cumulative Attribute Space for Age and Crowd Density Estimation—0
Curriculum for Crowd Counting -- Is it Worthy?—0
DAFE-FD: Density Aware Feature Enrichment for Face Detection—0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning—0
DeepCount: Crowd Counting with WiFi via Deep Learning—0
Deep Imbalanced Regression via Hierarchical Classification Adjustment—0
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks—0
Deep Spatial Regression Model for Image Crowd Counting—0
Dense Crowds Detection and Counting with a Lightweight Architecture—0
Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization—0
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