Crowd Counting

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Gaussian Density Splatting Network
Oct 7, 2026Miao Shang, Yabin Wang, Xiaopeng HongCrowd Counting Density Estimation
Faculty of Computing Harbin Institute of Technology
How Good Are Time-Series Foundation Models for Pedestrian Crowd Count Forecasting? A Cross-Dataset Comparative Study
Sep 14, 2026Theivaprakasham Hari, Ziteng Li, Yanan Xin +2Crowd Counting Time Series Forecasting
Department of Transport & Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
DCA-MoE: Spatially Adaptive Cross-Layer Fusion and Density-Routed Experts for Crowd Counting
Aug 15, 2026Hao WangCrowd Counting Multi-Scale Feature Fusion
BDNRC
HAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting
Jul 8, 2026Reem AlYabis, Fares AlTuwaim, AlJawharh AlOtaibi +1Crowd Counting Object Counting
Riyadh, Saudi Arabia
Multi-Modal Hyper-Graph Fusion for Low-Light Crowd Counting
Jun 17, 2026Hao-Yuan Ma, Li Zhang, Yushi Qiu +3Crowd Counting Deformable Attention
School of Computer Science and Technology, Soochow University, Suzhou, China
Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervision
Jun 12, 2026Amirah F. Alshammari, Bander A. Alzahrani, Nahed A. AlowidiSynthetic Data Augmentation Conditional Image Generation
Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia · Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
A Benchmark for Semi-supervised Multi-modal Crowd Counting
Jun 2, 2026Haoliang Meng, Xiaopeng Hong, Yabin Wang +1Crowd Counting Semi-Supervised Learning
Harbin Institute of Technology · Pengcheng Laboratory
Optimising CSRNet with parameter-free attention mechanisms for crowd counting in public transport
May 18, 2026Aida Rostamza, Enrico Del Re, Joshua Cherian Varughese +1Intelligent Transportation Systems Crowd Counting
Johannes Kepler University Linz, Austria, Department Intelligent Transport Systems, Altenberger Straße 69, 4040 Linz, Austria.
Thermal-Only Crowd Counting with Deployment-Time Privacy Protection
May 16, 2026Yifei Qian, Zhongliang Guo, Chun Tong Lei +4Thermal Imaging Crowd Counting
School of Computer Science, University of Nottingham, NG7 2RD Nottingham, U.K. · School of Computer Science, University of St Andrews, KY16 9AJ St Andrews, U.K. · Department of Data Science, City University of Hong Kong, Hong Kong SAR, China +1
RACANet: Reliability-Aware Crowd Anchor Network for RGB-T Crowd Counting
Apr 27, 2026Jinghao Shi, Mengqi Lei, Kunliang He +3Cross-Modal Alignment Crowd Counting
School of Computer Science, China University of Geosciences, Wuhan, Wuhan, China
Closed-Circuit Television Data as an Emergent Data Source for Urban Rail Platform Crowding Estimation
Aug 3, 2025Riccardo Fiorista, Awad Abdelhalim, Anson F. Stewart +3Intelligent Transportation Systems Crowd Counting
Transit Lab Department of Urban Studies and Planning Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Washington Metropolitan Area Transit Authority 300 7th Street SW, Washington, DC 20024, USA
ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation
Date pendingAnindya Mondal, Sauradip Nag, Anjan DuttaVLM Adaptation Crowd Counting
University of Surrey, United Kingdom & Simon Fraser University, Canada · University of Surrey, United Kingdom

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

Gaussian Density Splatting Network

Miao Shang, Yabin Wang, Xiaopeng Hong

Faculty of Computing Harbin Institute of Technology

Crowd Counting Density Estimation Gaussian Splatting

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This paper proposes a novel crowd counting approach, the Gaussian Density Splatting Network (GDSNet). Unlike methods that rely on conventional, grid-based density maps and are sensitive to spatial resolution, GDSNet represents a crowd as a superposition of continuous 2D Gaussian primitives. Our approach is built upon two key contributions. First, a control-point-based fitting mechanism is introduced to structure the prediction of Gaussian parameters. A set of control points is adaptively allocated to define local regions, from which features are pooled to regress each primitive's parameters. Second, we adapt a differentiable Gaussian Splatting framework to the counting task by parameterizing each primitive with geometric parameters and a scalar density mass. This formulation allows the network to be trained end-to-end via spatial matching of differentiably rendered density maps, naturally providing both local density supervision and global count optimization. Extensive evaluations on four standard benchmarks show GDSNet consistently outperforms the state of the art. The code is available at https://github.com/infinite0522/GDSNet-Gaussian-Density-Splatting-Network.

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