A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network.

ABSTRACT: This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully convolutional neural network (FCNN)-based method to monitor crowd analysis, especially for the classification of crowd density. This study aims to address the current technological challenges faced in

SUBMITTER: Bhuiyan MR 

PROVIDER: S-EPMC9044363 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature