Automated Crime Detection and Alert System using Embedded Deep Learning

In recent years, cc-tv cameras are used in various locations. The data captured by these cameras can be used for event prediction, real-time monitoring, and goal-oriented analysis, including anomaly and intrusion detection. With advancements in Artificial Intelligence, several methods are applied for anomaly detection, in which convolutional neural networks (CNNs) powered by deep-learning have improved detection accuracy. This article aims to introduce a novel deep learning- based approach for crime detection in video surveillance footage. this method has been tested on the UCSD dataset and has demonstrated improved accuracy in detecting criminal activities. Nowadays, various Artificial Intelligence techniques have been used to detect anomalies, amongst them convolutional neural networks using deep learning techniques improved the detection accuracy significantly. The goal of this article is to propose a new method based on deep learning techniques for Crime detection in video surveillance cameras. The proposed method has been evaluated in the UCSD dataset, and showed an increase in the accuracy of the Crime detection.