Autonomous Anomaly Detection System for Crime Monitoring and Alert Generation


Keywords: Autonomous vehicle, LSTM, Open CV, ECC, Crime and Streaming


Nowadays violence in society impacts more than any weapon used by people.  Over the last few years, autonomous vehicles have been used to observe and recognize the abnormality in the behavior of people and classifying them as a crime or not. Detecting crime on live stream is a classification of any event in a crime or not a crime and generating alerts to designated authorities which in turn can take required actions and ensure the security assessment of the city. Such kind of effective techniques for live video stream processing in computer vision is the need of the hour. There are many techniques that can be used, while Long Short-Term Memory (LSTM) networks and OpenCV provide the most accurate prediction for this task. OpenCV is used for the task of object detection in computer vision which will take the input from either drone or any autonomous vehicle. LSTM is used to classify any event or behavior in crime or not. This live stream is also encrypted for more security. To make this system more secure with the use of a security algorithm. This system uses the Elliptic curve algorithm to protect data from any kind of manipulation.

Through its ability to sense its surroundings, an autonomous vehicle is able to operate itself and execute critical activities without the need for human interaction. Video surveillance of computer vision technology can be used to take care of the security of the city by observing the crowd as well as individual activities. Much crowd-based crimes like mob lynching and individual crimes like murder, burglary, terrorism can be protected with advanced deep learning-based Anamoly detection techniques. Machine learning model use  dataset for learning is taken from different sources specific for crime detection with the 90% accuracy.





How to Cite

Kukade, J. ., Soner, S., & Pandya, S. (2023). Autonomous Anomaly Detection System for Crime Monitoring and Alert Generation. Journal of Automation, Mobile Robotics and Intelligent Systems, 16(1), 62-71.

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