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Crowd Counting

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.

Source: Deep Density-aware Count Regressor

Papers

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Showing 1–25 of 371 papers

TitleStatusHype
Inception-Based Crowd Counting -- Being Fast while Remaining AccurateCode4
Towards Automatic Power Battery Detection: New Challenge Benchmark Dataset and BaselineCode3
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise ClassificationCode2
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and LocalizationCode2
Improving Point-based Crowd Counting and Localization Based on Auxiliary Point GuidanceCode2
CNN-based Density Estimation and Crowd Counting: A SurveyCode2
Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and BaselineCode2
Single Domain Generalization for Crowd CountingCode2
HumanBench: Towards General Human-centric Perception with Projector Assisted PretrainingCode2
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
CCTrans: Simplifying and Improving Crowd Counting with TransformerCode1
Crowd Counting in the Frequency DomainCode1
Adaptive Mixture Regression Network with Local Counting Map for Crowd CountingCode1
CLIP-Count: Towards Text-Guided Zero-Shot Object CountingCode1
DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd CountingCode1
Cross-head Supervision for Crowd Counting with Noisy AnnotationsCode1
AdaCrowd: Unlabeled Scene Adaptation for Crowd CountingCode1
Counting People by Estimating People FlowsCode1
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd CountingCode1
Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark MethodCode1
A Self-Training Approach for Point-Supervised Object Detection and Counting in CrowdsCode1
Backdoor Attacks on Crowd CountingCode1
Boosting Crowd Counting via Multifaceted AttentionCode1
Boosting Detection in Crowd Analysis via Underutilized Output FeaturesCode1
Completely Self-Supervised Crowd Counting via Distribution MatchingCode1
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