People Counting and Tracking System in Real-Time Using Deep Learning Techniques

Abstract

Abstract: The aim of project is to automatically estimate the number of people at indoor and outdoor places. People counting systems can be used in retail environment such as determining conversion ratio, advertising and promotional evaluation. This system can be used for transportation management system and video surveillance. The number of customers is indispensable data for management and decision making in public places like large-scale markets, shopping centers, airports, stations, museums, laboratories, classrooms, cafeteria etc. In this system, firstly extract the frames from the video, then draw a desired reference line on the input frame, detect the people using MobileNet-SSD object detection model, mark the centroid on the detected person, track the movement of that marked centroid and calculate the direction of centroid movement whether it is moving upwards or downwards. If the centroid movement is downward direction, then increment in counter, else if the centroid movement is upward direction, then increment out counter. People counting is essential for retailers of any size, but it's especially important for small businesses that don’t have the benefit of assaying data from multitudinous locales whenmaking pivotal opinions. When used intelligently, people counting can shape businesses in multitudinous ways other than just furnishing information on nethermost business.