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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 301–350 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
Attention to Head Locations for Crowd Counting—0
Crowd Counting with Density Adaption Networks—0
Crowd Counting via Adversarial Cross-Scale Consistency PursuitCode0
Crowd Counting With Deep Negative Correlation LearningCode0
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images—0
Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid—0
A Deeply-Recursive Convolutional Network for Crowd Counting—0
A-CCNN: adaptive ccnn for density estimation and crowd counting—0
Improving Object Counting with Heatmap RegulationCode0
Leveraging Unlabeled Data for Crowd Counting by Learning to RankCode0
Depth Information Guided Crowd Counting for Complex Crowd Scenes—0
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested ScenesCode0
Structured Inhomogeneous Density Map Learning for Crowd Counting—0
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density EstimationCode0
Incorporating Side Information by Adaptive Convolution—0
Crowd Counting Through Walls Using WiFi—0
Crowd counting via scale-adaptive convolutional neural networkCode0
Deep Spatial Regression Model for Image Crowd Counting—0
Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models—0
Learning a perspective-embedded deconvolution network for crowd counting—0
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs—0
Switching Convolutional Neural Network for Crowd CountingCode0
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd CountingCode0
Spatiotemporal Modeling for Crowd Counting in Videos—0
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density EstimationCode0
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