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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