Crowd Counting
138 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
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.
Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning
In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale.
Application-Driven AI Paradigm for Person Counting in Various Scenarios
Person counting is considered as a fundamental task in video surveillance.
Crowd Counting with Online Knowledge Learning
Moreover, we propose a feature relation distillation method which allows the student branch to more effectively comprehend the evolution of inter-layer features by constructing a new inter-layer relationship matrix.
LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance
These models have achieved good accuracy over benchmark datasets.
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks
In this paper, we introduce PromptMix, a method for artificially boosting the size of existing datasets, that can be used to improve the performance of lightweight networks.
HDNet: A Hierarchically Decoupled Network for Crowd Counting
Recently, density map regression-based methods have dominated in crowd counting owing to their excellent fitting ability on density distribution.
