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

1
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea
2
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; https://doi.org/10.3390/s20010043
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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MDPI and ACS Style

Ilyas, N.; Shahzad, A.; Kim, K. Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation. Sensors 2020, 20, 43.

AMA Style

Ilyas N, Shahzad A, Kim K. Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation. Sensors. 2020; 20(1):43.

Chicago/Turabian Style

Ilyas, Naveed; Shahzad, Ahsan; Kim, Kiseon. 2020. "Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation" Sensors 20, no. 1: 43.

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