Abstract:
With the increased occurrences of crowd disasters like human stampedes, crowd management
and their safety during mass gathering events like concerts, congregation or political
rally, etc., are vital tasks for the security personnel. In this research work, we propose a
framework named as CrowdVAS-Net for crowd-motion analysis that considers velocity, acceleration
and saliency features in the video frames of a moving crowd. CrowdVAS-Net relies
on a deep convolutional neural network (DCNN) for extracting motion and appearance
feature representations from the video frames that helps us in classifying the crowd-motion
behaviour as abnormal or normal from a short video clip. These feature representations
are then trained with a random forest classifier. Furthermore, a dataset having 704 video
clips having dense crowded scenes has been created for performance evaluation of the proposed
method. Simulation results confirm that the proposed CrowdVAS-Net achieves the
classification accuracy of 77.8% outperforming the state-of-the-art machine learning models.
Moreover, this framework also reduced the video processing and analyzing time up
to 96.8% compared to the traditional method of video processing. Based on our results,
we believe that our work will help security personnel and crowd managers in ensuring the
public safety during mass gatherings with better accuracy
