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
Single Domain Generalization for Crowd Counting
The existing SDG approaches are mainly for image classification and segmentation, and can hardly be extended to our case due to its regression nature and label ambiguity (i. e., ambiguous pixel-level ground truths).
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
The CLIP (Contrastive Language-Image Pretraining) model has exhibited outstanding performance in recognition problems, such as zero-shot image classification and object detection.
Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting
csguomy/mdknet
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Multidomain crowd counting aims to learn a general model for multiple diverse datasets.
Gramformer: Learning Crowd Counting via Graph-Modulated Transformer
The graph is building upon the dissimilarities between patches, modulating the attention in an anti-similarity fashion.
Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and Baseline
xiaoqi-zhao-dlut/x-ray-pbd
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We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries.
Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel
yeyimilk/deep-learning-for-manatee-counting
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In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input.
Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes
To alleviate the heavy annotation burden for training a reliable crowd counting model and thus make the model more practicable and accurate by being able to benefit from more data, this paper presents a new semi-supervised method based on the mean teacher framework.
SYRAC: Synthesize, Rank, and Count
To address this, we use latent diffusion models to create two types of synthetic data: one by removing pedestrians from real images, which generates ranked image pairs with a weak but reliable object quantity signal, and the other by generating synthetic images with a predetermined number of objects, offering a strong but noisy counting signal.
Boosting Detection in Crowd Analysis via Underutilized Output Features
wskingdom/crowd-hat
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CVPR 2023
Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds.
Point-Query Quadtree for Crowd Counting, Localization, and More
cxliu0/pet
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ICCV 2023
Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable.
