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Deep Trajectory Classification Model for Congestion Detection in Human Crowds

Emad Felemban1, Sultan Daud Khan2, Atif Naseer3, Faizan Ur Rehman4,*, Saleh Basalamah1

1 Department of Computer Engineering, College of Computing and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
2 Department of Computer Science, National University of Technology, Islamabad, Pakistan
3 Science and Technology Unit, Umm Al-Qura University, Makkah, Saudi Arabia
4 Institute of Consulting Research and Studies, Umm Al-Qura University, Makkah, Saudi Arabia

* Corresponding Author: Faizan Ur Rehman. Email: email

Computers, Materials & Continua 2021, 68(1), 705-725.


In high-density gatherings, crowd disasters frequently occur despite all the safety measures. Timely detection of congestion in human crowds using automated analysis of video footage can prevent crowd disasters. Recent work on the prevention of crowd disasters has been based on manual analysis of video footage. Some methods also measure crowd congestion by estimating crowd density. However, crowd density alone cannot provide reliable information about congestion. This paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian trajectories. The proposed framework divided the input video into several temporal segments. We then extracted dense trajectories from each temporal segment and converted these into a spatio-temporal image without losing information. A classification model based on convolutional neural networks was then trained using spatio-temporal images. Next, we generated a score map by encoding each point trajectory with its respective class score. After this, we obtained the congested regions by employing the non-maximum suppression method on the score map. Finally, we demonstrated the proposed framework’s effectiveness by performing a series of experiments on challenging video sequences.


Cite This Article

E. Felemban, S. Daud Khan, A. Naseer, F. Ur Rehman and S. Basalamah, "Deep trajectory classification model for congestion detection in human crowds," Computers, Materials & Continua, vol. 68, no.1, pp. 705–725, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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