AI and Machine Learning for Remote Suspicious Action Detection and Recognition

Abstract

There is little question that the unchecked rise in population is to blame for the alarming increase in crime rates seen in industrialized and developing nations. As a direct consequence of this, there has been an increase in the number of calls for the use of video surveillance to address concerns about ordinary life and private property. As a consequence of this, we need a system that is capable of accurately recognizing human activity in real-time. Researchers have lately investigated machine learning and deep learning as potential methods for identifying human activities. To prevent fraud, we devised a technique that employs human activity recognition to examine a series of occurrences, evaluate whether or not a person is a suspect, and then take appropriate action. This system used deep learning to assign labels to the video based on human behavior. We can detect suspicious behavior based on the categories mentioned above of human activity and time duration by utilizing machine learning, which achieves an accuracy of around one hundred percent. This research article will detect suspicious behavior using optimal, effective, and quick methods. Using popular public data sets, the experimental findings described here highlight the approach's remarkable performance while only requiring a small amount of computational complexity.