{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:06:27Z","timestamp":1773486387191,"version":"3.50.1"},"reference-count":155,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,19]],"date-time":"2019-12-19T00:00:00Z","timestamp":1576713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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).<\/jats:p>","DOI":"10.3390\/s20010043","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T03:15:01Z","timestamp":1577070901000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8220-4474","authenticated-orcid":false,"given":"Naveed","family":"Ilyas","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5429-3011","authenticated-orcid":false,"given":"Ahsan","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Engineering (DCSE), College of Electrical and Mechanical Engineering (EME), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}]},{"given":"Kiseon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"034003","DOI":"10.1117\/1.JMI.1.3.034003","article-title":"Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features","volume":"1","author":"Wang","year":"2014","journal-title":"J. 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