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Deep Learning for People Counting Model

  • T. RevathiEmail author
  • T. M. Rajalaxmi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)

Abstract

Modeling of automatic people detection and counting in a real-time video is an important feature in a smart surveillance system for safety and security management, marketing research, etc. Face recognition is one of the methods which is used for people detection. In this paper, a real-time automated model is designed using deep learning algorithm such as convolutional neural network which is computationally efficient. The face detected using the proposed algorithm exploits the challenges such as variations in size and shape of the head region to achieve robust detection of a human, even under partial occlusion, dynamically changing background, and varying illumination condition. Here, we have used WIDER face dataset and FDDB dataset to show the results of the proposed method.

Keywords

Face detection Surveillance GMM Foreground detection CNN 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceSSN College of EngineeringChennaiIndia
  2. 2.Department of MathematicsSSN College of EngineeringChennaiIndia

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