ReviewSensors (Basel, Switzerland)2019
Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation.
Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application.Journal of translational medicine · 2026Review
- Auxiliary diagnosis of periprosthesis joint infection by leukocyte esterase strips test using a deep learning model.International journal of surgery (London, England) · 2025Article
- Cultivation strategies of English thinking ability in the environment of Internet of Things.Heliyon · 2024Article
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions.Sensors (Basel, Switzerland) · 2024Review
- Article
- Deep learning for MRI lesion segmentation in rectal cancer.Frontiers in medicine · 2024Review
- Early warning on safety risk of highly aggregated tourist crowds based on VGGT-Count network model.PloS one · 2024Article
- Design of a Femtosecond Laser Percussion Drilling Process for Ni-Based Superalloys Based on Machine Learning and the Genetic Algorithm.Micromachines · 2023Article
- Automation ofComputational and structural biotechnology journal · 2023Article
- Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules.Scientific reports · 2022Article
- Prediction of Deformation-Induced Martensite Start Temperature by Convolutional Neural Network with Dual Mode Features.Materials (Basel, Switzerland) · 2022Article
- An effective modular approach for crowd counting in an image using convolutional neural networks.Scientific reports · 2022Article
- Article
- Congested Crowd Counting via Adaptive Multi-Scale Context Learning.Sensors (Basel, Switzerland) · 2021Article
- HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting.Sensors (Basel, Switzerland) · 2021Article
- Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network.Computational intelligence and neuroscience · 2021Article
- An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities.Sensors (Basel, Switzerland) · 2020Article
- LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture.Sensors (Basel, Switzerland) · 2020Article
- Narrative review on artificially intelligent olfaction in halitosis.Journal of oral and maxillofacial pathology : JOMFPReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.