COMPUTER VISION-BASED OCCUPANT BEHAVIOR TOWARDS ENERGY IN COMMERCIAL PREMISES USING CCTV FOOTAGE

Abstract

The rapid advancement of Internet of Things (IoT) technology and the avail ability of high-resolution CCTV have opened up new possibilities for analyzing occupant behavior in relation to energy consumption. This research paper introduces a computer vision-based system designed to monitor occupant behavior within commercial premises using CCTV footage. By leveraging computer vision techniques, the system effectively detects and tracks the movements of occupants while also predicting their behavior. Additionally, the system takes into account the lighting conditions within the premises and utilizes this information to make predictions about occupant behavior. Occupant behavior is classified into three distinct categories: good, moderate, and bad, based on their energy efficient practices. This classification allows commercial buildings to take appropriate measures against individuals exhibiting unfavorable behavior or to implement automated energy-saving solutions within their offices. Ultimately, the proposed system holds the potential to enhance energy efficiency and security within commercial buildings.

Key words: Window Person Indoor Energy Occupant.