Papers Crowd Counting
“Crowd Counting” 태그가 달린 논문 404편 · 필터 해제
Validated Adaptation for Aerial Crowd Monitoring at Mass Gathering Scale: A Deployment Protocol, a Severity Law, and a Diagnostic for Label-Free Drone Crowd Counting, Toward the FIFA World Cup 2034 (Saudi Arabia)
Saudi Arabia will host the 2034 FIFA World Cup and already operates crowd management at Hajj scale. Drone-based counting must hold accuracy on footage unlike anything in its training corpus, without labels, and must warn…
Crowd CountingHAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting
Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertic…
Crowd CountingABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation
ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image generation without any benchmark-specific training required. Our model is bu…
Object LocalizationImage GenerationObject CountingCrowd CountingMulti-Modal Hyper-Graph Fusion for Low-Light Crowd Counting
Crowd counting is a fundamental task in computer vision. However, crowd counting in low-light environments remains largely underexplored, despite its practical importance in the real world. Existing methods mainly focus …
Crowd CountingA Benchmark for Semi-supervised Multi-modal Crowd Counting
This paper constructs the first benchmark on semi-supervised multi-modal crowd counting. To lay the foundation for this unexplored task, we first formulate the semi-supervised multi-modal setting and a standardized proto…
Crowd CountingCounting Machine Parts
Counting objects in an image is a task applicable across many domains. For instance, crowd counting, inventory counting, and cell counting have been the focus of recent research. The major challenges in estimating the co…
Instance SegmentationCrowd CountingOptimising CSRNet with parameter-free attention mechanisms for crowd counting in public transport
Occupancy estimation and crowd counting are critical tasks in designing smart and efficient public transport vehicles. Given that public transport loading can vary from sparse to crowded, classical models for occupancy e…
Crowd CountingThermal-Only Crowd Counting with Deployment-Time Privacy Protection
While RGB-Thermal crowd counting has shown promise, the paradigm faces critical limitations: RGB data raises privacy concerns in public surveillance, and multi-modal misalignment degrades fusion performance. We propose t…
Crowd CountingRACANet: Reliability-Aware Crowd Anchor Network for RGB-T Crowd Counting
RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation in complex scenes. Although existing studies generally improve coun…
Density EstimationCrowd CountingSynMVCrowd: A Large Synthetic Benchmark for Multi-view Crowd Counting and Localization
Existing multi-view crowd counting and localization methods are evaluated under relatively small scenes with limited crowd numbers, camera views, and frames. This makes the evaluation and comparison of existing methods i…
Data AugmentationCrowd CountingGranular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting
Single-source domain generalization for crowd counting is highly challenging because a single labeled source domain may contain heterogeneous latent domains, while unseen target domains often exhibit severe distribution …
Single-Source Domain GeneralizationCrowd CountingGenerative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting
State-of-the-art crowd counting and localization are primarily modeled using two paradigms: density maps and point regression. Given the field's security ramifications, there is active interest in model robustness agains…
Crowd CountingExclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting
Semi-supervised crowd analysis is a prominent area of research, as unlabeled data are typically abundant and inexpensive to obtain. However, traditional point-based annotations constrain performance because individual re…
Instance SegmentationCrowd CountingPhys-3D: Physics-Constrained Real-Time Crowd Tracking and Counting on Railway Platforms
Accurate, real-time crowd counting on railway platforms is essential for safety and capacity management. We propose to use a single camera mounted in a train, scanning the platform while arriving. While hardware constrai…
Crowd CountingOne-Shot Crowd Counting With Density Guidance For Scene Adaptation
Crowd scenes captured by cameras at different locations vary greatly, and existing crowd models have limited generalization for unseen surveillance scenes. To improve the generalization of the model, we regard different …
Few-Shot LearningCrowd CountingFSCA-Net: Feature-Separated Cross-Attention Network for Robust Multi-Dataset Training
Crowd counting plays a vital role in public safety, traffic regulation, and smart city management. However, despite the impressive progress achieved by CNN- and Transformer-based models, their performance often deteriora…
Crowd CountingRepSFNet : A Single Fusion Network with Structural Reparameterization for Crowd Counting
Crowd counting remains challenging in variable-density scenes due to scale variations, occlusions, and the high computational cost of existing models. To address these issues, we propose RepSFNet (Reparameterized Single …
Crowd CountingVideo Individual Counting and Tracking from Moving Drones: A Benchmark and Methods
Counting and tracking dense crowds in large-scale scenes is valuable yet challenging, while existing methods and datasets are largely limited to fixed cameras with small scene coverage. We introduce MovingDroneCrowd++, a…
Crowd CountingCrowded Video Individual Counting Informed by Social Grouping and Spatial-Temporal Displacement Priors
Video Individual Counting (VIC) is a recently introduced task aiming to estimate pedestrian flux from a video. It extends Video Crowd Counting (VCC) beyond the per-frame pedestrian count. In contrast to VCC that learns t…
Crowd CountingTCFormer: A 5M-Parameter Transformer with Density-Guided Aggregation for Weakly-Supervised Crowd Counting
Crowd counting typically relies on labor-intensive point-level annotations and computationally intensive backbones, restricting its scalability and deployment in resource-constrained environments. To address these challe…
Crowd Counting