CNN-based Density Estimation and Crowd Counting: A Survey

Abstract

Accurately estimating the number of objects in a single image is a challenging yet meaningful task and has been applied in many applications such as urban planning and public safety. In the various object counting tasks, crowd counting is particularly prominent due to its specific significance to social security and development. Fortunately, the development of the techniques for crowd counting can be generalized to other related fields such as vehicle counting and environment survey, if without taking their characteristics into account. Therefore, many researchers are devoting to crowd counting, and many excellent works of literature and works have spurted out. In these works, they are must be helpful for the development of crowd counting. However, the question we should consider is why they are effective for this task. Limited by the cost of time and energy, we cannot analyze all the algorithms. In this paper, we have surveyed over 220 works to comprehensively and systematically study the crowd counting models, mainly CNN-based density map estimation methods. Finally, according to the evaluation metrics, we select the top three performers on their crowd counting datasets and analyze their merits and drawbacks. Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields. We provide the density maps and prediction results of some mainstream algorithm in the validation set of NWPU dataset for comparison and testing. Meanwhile, density map generation and evaluation tools are also provided. All the codes and evaluation results are made publicly available at https://github.com/gaoguangshuai/survey-for-crowd-counting.

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Cited by in corpus (15)

• DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation
• Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review
• Deep learning with self-supervision and uncertainty regularization to count fish in underwater images
• Learning Independent Instance Maps for Crowd Localization
• Density-based clustering with fully-convolutional networks for crowd flow detection from drones
• Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
• Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting
• Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
• Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization
• Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
• Counting People by Estimating People Flows
• Bio-inspired visual relative localization for large swarms of UAVs
• Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting
• VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
• Harnessing Perceptual Adversarial Patches for Crowd Counting