A Review on Intelligent Crowd Counting System with Gender Classification

  • Dr.Sheshang Degadwala et al.

Abstract

Estimating the number of people in highly clustered crowd scenes is an extremely challenging task on account of serious occlusion and non-uniformity distribution in one crowd image. People-Counting technology can be generalized into two kinds of literature: detection methods and counting methods. Traditional approaches for crowd counting from images relied on hand-crafted representations to extract low-level features. These features were then mapped for counting or generating density maps via various counting techniques. The detection-based model typically employs sliding window-based detection algorithms to count people in an image. This Project also comparison of different gender classification techniques and use of different racial features such as eyes, nose, and mouth etc. for gender classification its applications in many areas like monitoring, surveillance, and commercial profiling and human-computer interaction.

Published
2019-10-08
Section
Articles