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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 301–325 of 371 papers

TitleStatusHype
DeepCount: Crowd Counting with WiFi via Deep Learning—0
Learning from Synthetic Data for Crowd Counting in the Wild—0
Crowd Counting Using Scale-Aware Attention Networks—0
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network—0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Dual Path Multi-Scale Fusion Networks with Attention for Crowd CountingCode0
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale UpsamplingCode0
Multi-Scale Attention Network for Crowd Counting—0
DAFE-FD: Density Aware Feature Enrichment for Face Detection—0
Mask-aware networks for crowd counting—0
Adaptive Scenario Discovery for Crowd CountingCode0
ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd UnderstandingCode0
Generalizing semi-supervised generative adversarial networks to regression using feature contrasting—0
Context-Aware Crowd CountingCode0
PaDNet: Pan-Density Crowd Counting—0
Class-Agnostic CountingCode0
Scale Aggregation Network for Accurate and Efficient Crowd CountingCode0
Stacked Pooling: Improving Crowd Counting by Boosting Scale InvarianceCode0
In Defense of Single-column Networks for Crowd Counting—0
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds—0
Iterative Crowd Counting—0
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN—0
Top-Down Feedback for Crowd Counting Convolutional Neural Network—0
Revisiting Perspective Information for Efficient Crowd Counting—0
Crowd Counting using Deep Recurrent Spatial-Aware Network—0
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