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Title	Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, And Performance Evaluation
ID_Doc	16111
Authors	Ilyas N.; Shahzad A.; Kim K.
Year	2020
Published	Sensors (Switzerland), 20, 1
DOI	http://dx.doi.org/10.3390/s20010043
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). © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords	Crowd analysis; Deep learning; Smart cities

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