International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 2, April 2021, pp. 1510~1520 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i2.pp1510-1520  1510 Journal homepage: http://ijece.iaescore.com Real-time human detection for electricity conservation using pruned-SSD and arduino Ushasukhanya S. 1 , Jothilakshmi S. 2 1 Department of Computer Science and Engineering, Annamalai University, Tamilnadu, India 2 Department of Information Technology, Annamalai University, Tamilnadu, India Article Info ABSTRACT Article history: Received Jul 1, 2020 Revised Aug 2, 2020 Accepted Sep 24, 2020 Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame. Keywords: Arduino Electricity management Human detection Pruning Single shot detection This is an open access article under the CC BY-SA license. Corresponding Author: Ushasukhanya S. Department of Computer Science and Engineering Annamalai University Annamalai nagar, Tamilnadu, India Email: ushasukhanya@gmail.com 1. INTRODUCTION Unmanned electric power management system (UEPMS) is one of the challenging tasks that are required to save electrical resources in most of the countries. Several researches are going on for UEPMS with the help of sensors to detect the presence/absence of humans and to manage the power supply accordingly. Usage of sensors incurs an additional cost and hence this work aims to develop a system that manages the electrical resource using the existing closed circuit television (CCTV) surveillance camera. The surveillance video captured from CCTV cameras is used to detect the human’s presence/absence to enable/disable the power supply thereby avoiding additional cost. Human detection in surveillance cameras footage has been an interesting [1] and challenging [2] topic in the recent years. The traditional hand-crafted methods like local binary pattern (LBP), histogram of oriented gradients (HOG) etc., are time consuming and proved to be comparatively inefficient to the recent convolutional neural network (CNN) based algorithms [3]. Various object detection algorithms in deep learning (DL) have shown promising results in classifying and detecting the location of the objects [4]. The first category of DNN is a two stage approach like RCNN, faster-regional CNN (faster R-CNN), region based fully convolutional neural network (R-FCN) [5, 6] etc.,