Predict, Identify and Alert on Suspicious Activity by Multiple Zone

Abstract

Suspicious human activity detection in security capture is a study topic in image processing and vision. The mysterious identification of human activity from video surveillance is an area of study in both fields. Human activity can be monitored visually in conspicuous public spaces like bus depots, airports, railway stations, financial institutions, malls, schools, and universities to avoid terrorist activity, vandalism, accidents, prohibited parking spaces, vandalism, fighting chain theft, criminality, and other unusual behavior. Extremely difficult to continually monitor public spaces, thereby an innovative video surveillance installation 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 in the literature for recognising various abnormal activities, but none have reviewed various abnormal activities, but none of them have reviewed various abnormal activities. This study presents the stateof-the-art in the field of recognizing suspicious behavior from surveillance recordings during the past tenyears. We provide a brief outline of the risks and challenges associated with detecting suspicious human activity. This article examines six aberrant behaviors, 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 ofthe 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 identificationsystems together with its broad framework.[1]