Predict, Identify and Alert on Suspicious Activity by Multiple Zone

Abstract

Abstract: An active topic of image processing and computer vision research is the detection of suspicious human behaviour in surveillance footage. In order to stop terrorism, theft,accidents, illegal parking, vandalism, fighting, chain snatching, crime, and other suspicious activities, human activity can be observed through visual surveillance in sensitive and public places like bus stations, railway stations, airports, banks, shopping malls, schools, and colleges. It is highly challenging to continually monitor public spaces, thus an intelligent video surveillance system that can track people's movements in real-time, classify them as routine or odd, and send out an alert is needed. The field of visual surveillance to identify aberrant actions has seen a significant amount of publications in the last ten years. Furthermore There are a few surveys that canbe found in the literature for the recognition of various abnormal activities, but none of them have reviewed various abnormal activities. This study presents the state-of-the-art in the field of recognising suspicious behaviour from surveillance recordings during the past tenyears. We give a quick overview of the concernsand difficulties associated in recognising suspicious human activity. This article examines six aberrant behaviours, including the identification of abandoned objects, theft, falls, traffic accidents, and unlawful parking, as wellas the detection of violence and fire. Generally speaking, we have covered all the processes that have been [1] Foreground object extraction, object identification based on tracking or non-tracking approaches, feature extraction, classification, activity analysis, and recognition are some of the techniques that have been used to identify human activity from surveillance movies in the literature. This paper's goal is to give field researchers a literature assessment of six different suspicious activity identification systems together with its broad framework.[1]