Detection and Identification of Unattended Objects in Video Surveillance using Machine Learning

Abstract

Video surveillance frameworks, acumen insight structures utilized in places for example railway stations, airports or various public places, can be helped to carry security to an upper level. The frameworks for video perception that are used for security reasons need advanced intellectual and robust technical ordinance. As the distress about the security across the world is soaring, it has raised the need to have installation of potential danger acknowledgment structures. This project depicts a framework which helps in recognizing the event of an individual leaving baggage unattended at a public place either deliberately or erroneously. Tracking of purposely left baggage is a significant issue as it obtrudes consequential security threats in crowded public transportation destinations in nations like India which has the biggest railway framework. The proposed system summons a caution whenever an abandoned baggage is encountered and also identifies time and date when the baggage was abandoned.