Crowd Counting
137 papers with code • 10 benchmarks • 19 datasets
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.
Benchmarks
These leaderboards are used to track progress in Crowd Counting
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Libraries
Use these libraries to find Crowd Counting models and implementationsDatasets
ShanghaiTech
UCF-QNRF
JHU-CROWD++
UCF-CC-50
NWPU-Crowd
JHU-CROWD
Latest papers with no code
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis
Simulation is a powerful tool to easily generate annotated data, and a highly desirable feature, especially in those domains where learning models need large training datasets.
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
In this study, we propose mutual prompt learning (mPrompt), which leverages a regressor and a segmenter as guidance for each other, solving bias and inaccuracy caused by annotation variance while distinguishing foreground from background.
Learning Discriminative Features for Crowd Counting
Crowd counting models in highly congested areas confront two main challenges: weak localization ability and difficulty in differentiating between foreground and background, leading to inaccurate estimations.
Deep Imbalanced Regression via Hierarchical Classification Adjustment
To improve regression performance over the entire range of data, we propose to construct hierarchical classifiers for solving imbalanced regression tasks.
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs
Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs).
Calibrating Uncertainty for Semi-Supervised Crowd Counting
A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set.
Counting Crowds in Bad Weather
Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding.
Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement
The increasing prevalence of gigapixel resolutions has presented new challenges for crowd counting.
Why Existing Multimodal Crowd Counting Datasets Can Lead to Unfulfilled Expectations in Real-World Applications
The key components of the monomodal architecture are also used in the multimodal architectures to be able to answer whether multimodal models perform better in crowd counting in general.
Crowd Counting with Sparse Annotation
This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image.
