Vision Based Algorithm for People Counting Using Deep Learning

Abstract

Estimating the number of people in a particular scene has always been an important topic of research in computer vision and digital image processing. People counting has wide applications in scenario ranging from analyzing the customer's choice and improving the quality of service in retail stores, supermarkets and shopping malls to managing human resources and optimizing the energy usage in office buildings. While there exists algorithms for counting people in a scene, some algorithm have set their benchmark in performance with respect to efficiency, flexibility and accuracy. In this paper, an attempt has been made to perform people counting using Deep Neural Networks (DNN) on comparison with existing image processing based algorithms like Histogram of Oriented Gradients with Support Vector Machine (HoG with SVM), Local Binary Pattern (LBP) based Adaboost classifier and contour based people detection. The proposed DNN based approach has higher accuracy at 90% and less false negatives.

Improvement of Deep Learning-based Human Detection using Dynamic Thresholding for Intelligent Surveillance System

“…Human detection plays an important role in many applications of ISS, such as person re-identification [2], human tracking [3], people counting [4], human action recognition [5], unattended baggage detection [1], etc. Even though many studies have been carried out for human detection, research on this topic still faces many challenges to overcome real problems constantly changing.…”

“…In many cases, human detection is used at an early stage and significantly affects the accuracy of ISS applications [1][2][3][4][5]. If the human detection accuracy is good, these applications will also produce a good performance and vice versa.…”

Adoption Factors of Artificial intelligence in Human Resource Management

“…Moreover, Shanmugam and Garg (2015) refer to AI as a tool to minimise the appraisal bias and to support both employees and management to understand the progress and shortcomings of an individual's performance. Furthermore, Padmashini et al (2018) highlight vision-based algorithms using DL when managing human resources. In contrast, this research work highlights the extending role of AI to intellectually handle the demanding tasks that require a degree of autonomy without human intervention (Willcocks et al, 2017;Xu et al, 2020) by grouping the most common applications of AI into HRM as follows.…”