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Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
Department of Computer and Software Engineering (DCSE), College of Electrical and Mechanical Engineering (EME), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 43;
Received: 23 November 2019 / Revised: 10 December 2019 / Accepted: 13 December 2019 / Published: 19 December 2019
(This article belongs to the Special Issue Applications of IoT and Machine Learning in Smart Cities)
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). View Full-Text
Keywords: deep learning; crowd analysis; smart cities deep learning; crowd analysis; smart cities
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Ilyas, N.; Shahzad, A.; Kim, K. Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation. Sensors 2020, 20, 43.

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