Crowd Monitoring using HOG


Authors : Sri.N.V.Phani Sai Kumar; Y. Sriram Kalyan; U. Akshaya; V. Lakshmi Pravallika; Zayer Sakeena

Volume/Issue : Volume 8 - 2023, Issue 1 - January

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3JACfff

DOI : https://doi.org/10.5281/zenodo.7599212

Abstract : Many security and event management agencies throughout the world are beginning to understand the significance of crowd surveillance as public safety concerns increase. These organisations can avert any unforeseen mishaps or problems by estimating crowd dynamics. The goal of this research is to develop a system that can more effectively monitor crowds utilising Support Vector Machine (SVM) classifiers and Histogram of Oriented Gradients (HOG) features. According to our needs, we can interface two or more cameras to count the number of individuals in the input video of the cameras and to identify their locations in 3D space. This provides a sense of the density.

Many security and event management agencies throughout the world are beginning to understand the significance of crowd surveillance as public safety concerns increase. These organisations can avert any unforeseen mishaps or problems by estimating crowd dynamics. The goal of this research is to develop a system that can more effectively monitor crowds utilising Support Vector Machine (SVM) classifiers and Histogram of Oriented Gradients (HOG) features. According to our needs, we can interface two or more cameras to count the number of individuals in the input video of the cameras and to identify their locations in 3D space. This provides a sense of the density.

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe