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  • Open Access


    Multi-Scale Network with Integrated Attention Unit for Crowd Counting

    Adel Hafeezallah1, Ahlam Al-Dhamari2,3,*, Syed Abd Rahman Abu-Bakar2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3879-3903, 2022, DOI:10.32604/cmc.2022.028289

    Abstract Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering. Moreover, since the crowd images in this case can range from low density to high density, detection-based approaches are hard to apply for crowd counting. Recently, deep learning-based regression has become the prominent approach for crowd counting problems, where a density-map is estimated, and its integral is further computed to acquire the final count result. In this paper, we put forward a novel multi-scale network (named 2U-Net)… More >

  • Open Access


    Identification and Classification of Crowd Activities

    Manar Elshahawy1, Ahmed O. Aseeri2,*, Shaker El-Sappagh3,4, Hassan Soliman1, Mohammed Elmogy1, Mervat Abu-Elkheir5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 815-832, 2022, DOI:10.32604/cmc.2022.023852

    Abstract The identification and classification of collective people's activities are gaining momentum as significant themes in machine learning, with many potential applications emerging. The need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd congestion. This paper investigates the capability of deep neural network (DNN) algorithms to achieve our carefully engineered pipeline for crowd analysis. It includes three principal stages that cover crowd analysis challenges. First, individual's detection is represented using the You Only Look Once (YOLO) model for human detection and Kalman filter for multiple human tracking; Second, the density… More >

  • Open Access


    Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

    Durr-e-Nayab1, Ali Mustafa Qamar2,*, Rehan Ullah Khan3, Waleed Albattah3, Khalil Khan4, Shabana Habib3, Muhammad Islam5

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5581-5601, 2022, DOI:10.32604/cmc.2022.022153

    Abstract The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a… More >

  • Open Access


    Utilization of Deep Learning-Based Crowd Analysis for Safety Surveillance and Spread Control of COVID-19 Pandemic

    Osama S. Faragallah1,*, Sultan S. Alshamrani1, Heba M. El-Hoseny2, Mohammed A. AlZain1, Emad Sami Jaha3, Hala S. El-Sayed4

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1483-1497, 2022, DOI:10.32604/iasc.2022.020330

    Abstract Crowd monitoring analysis has become an important challenge in academic researches ranging from surveillance equipment to people behavior using different algorithms. The crowd counting schemes can be typically processed in two steps, the images ground truth density maps which are obtained from ground truth density map creation and the deep learning to estimate density map from density map estimation. The pandemic of COVID-19 has changed our world in few months and has put the normal human life to a halt due to its rapid spread and high danger. Therefore, several precautions are taken into account during COVID-19 to slowdown the… More >

  • Open Access


    Deep Trajectory Classification Model for Congestion Detection in Human Crowds

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

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 705-725, 2021, DOI:10.32604/cmc.2021.015085

    Abstract 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… More >

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