Suspicious Activity Detection Using Machine Learning

Abstract

Abstract: Various technologies have been utilized to implement the safety of life and property byinstalling high quality CCTV cameras. It is not possible to manually monitor each and every moment activity. Furthermore, in practical scenario the most unpredictable one is human behaviour and it is very difficult to find whether it is suspicious or normal. In this work the notion of CNN is used to detect suspicious or normal activity in an environment, and a systemis proposed that sends an alert message to the similarity authority, in case of predicting a suspicious activity. It's worth noting that the effectiveness of a suspicious activity detection system relies on the quality of the training data, the architecture of the Machine Learning model, and the deployment environment. Ongoing monitoring, regular updates, and continuousimprovement are important for maintaining the system's accuracy and adapting it to new and emerging types of suspicious activities