# A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation

arXiv: https://arxiv.org/abs/1707.01202 · HTML: https://mlsanity.com/paper/1707.01202/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2017.

## Built on by (most cited first, 29 of 29)

- [CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model](https://mlsanity.com/paper/2304.04231/) (2023)
- [STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning](https://mlsanity.com/paper/2308.10468/) (2023)
- [Single-Layer Vision Transformers for More Accurate Early Exits with Less Overhead](https://mlsanity.com/paper/2105.09121/) (2021)
- [T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting](https://mlsanity.com/paper/2502.20625/) (2025)
- [Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives](https://mlsanity.com/paper/2209.07271/) (2022)
- [Multi-Camera Vehicle Counting Using Edge-AI](https://mlsanity.com/paper/2106.02842/) (2021)
- [A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches](https://mlsanity.com/paper/2501.19184/) (2025)
- [Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting](https://mlsanity.com/paper/2104.10868/) (2021)
- [Crowd counting with crowd attention convolutional neural network](https://mlsanity.com/paper/2204.07347/) (2022)
- [Counting and Locating High-Density Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2102.04366/) (2021)
- [Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks](https://mlsanity.com/paper/2212.11542/) (2022)
- [Target-agnostic Source-free Domain Adaptation for Regression Tasks](https://mlsanity.com/paper/2312.00540/) (2023)
- [Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio](https://mlsanity.com/paper/2111.12888/) (2021)
- [An Improved Normed-Deformable Convolution for Crowd Counting](https://mlsanity.com/paper/2206.08084/) (2022)
- [Visual Crowd Analysis: Open Research Problems](https://mlsanity.com/paper/2308.10677/) (2023)
- [Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting](https://mlsanity.com/paper/2104.02245/) (2021)
- [Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems](https://mlsanity.com/paper/2201.00063/) (2021)
- [Bio-inspired visual relative localization for large swarms of UAVs](https://mlsanity.com/paper/2412.02393/) (2024)
- [Weakly-supervised multi-class object localization using only object counts as labels](https://mlsanity.com/paper/2102.11743/) (2021)
- [Multi-channel Deep Supervision for Crowd Counting](https://mlsanity.com/paper/2103.09553/) (2021)
- [Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification](https://mlsanity.com/paper/2105.09684/) (2021)
- [Crowd counting with segmentation attention convolutional neural network](https://mlsanity.com/paper/2204.07380/) (2022)
- [A Unified Object Counting Network with Object Occupation Prior](https://mlsanity.com/paper/2212.14193/) (2022)
- [Density-based clustering with fully-convolutional networks for crowd flow detection from drones](https://mlsanity.com/paper/2301.04937/) (2023)
- [Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives](https://mlsanity.com/paper/2307.13541/) (2023)
- [DSGC-Net: A Dual-Stream Graph Convolutional Network for Crowd Counting via Feature Correlation Mining](https://mlsanity.com/paper/2509.02261/) (2025)
- [RACANet: Reliability-Aware Crowd Anchor Network for RGB-T Crowd Counting](https://mlsanity.com/paper/2604.24543/) (2026)
- [Object Counting Across Modalities: Taxonomies, Benchmarks, Applications, and Open Challenges](https://mlsanity.com/paper/2608.23845/) (2026)
- [DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting](https://mlsanity.com/paper/2609.17613/) (2026)

## Abstract (arXiv)

Estimating count and density maps from crowd images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. In addition, techniques developed for crowd counting can be applied to related tasks in other fields of study such as cell microscopy, vehicle counting and environmental survey. The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios. The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets. In this paper, we provide a comprehensive survey of recent Convolutional Neural Network (CNN) based approaches that have demonstrated significant improvements over earlier methods that rely largely on hand-crafted representations. First, we briefly review the pioneering methods that use hand-crafted representations and then we delve in detail into the deep learning-based approaches and recently published datasets. Furthermore, we discuss the merits and drawbacks of existing CNN-based approaches and identify promising avenues of research in this rapidly evolving field.