Kahlil Muchtar, Adhiguna Mahendra, Muhammad Rizky Munggaran, Maya Fitria, Al Bahri, Fitri Arnia, Chih-Yang Lin
The use of high-performance computing devices to detect anomalies in public spaces is expensive and impractical. Innovative improvements, especially hardware acceleration in embedded systems, are significantly impacting the deployment of deep learning in the real world. In terms of edge computing, the Jetson Platform by NVIDIA is one of the leading platforms that provide optimal energy efficiency, required accuracy, and throughput when running deep learning algorithms. Therefore, we offer the use of anomaly detection on edge devices to perform intelligent monitoring. Deploying an AI-powered anomaly detector at the edge allows initial analysis to be executed at the captured image's location, reducing communication overhead and enabling a fast security response. On the edge device side, we prepared an I3D (Inflated 3D CNN) model that can detect anomaly events. To be specific, the edge device used NVIDIA® Jetson Orin NanoTM 8GB. The model output the probability of anomaly events, along with the confidence percentage and estimated occurred frames. Finally, for real-time analysis, we built a GUI-friendly web interface through flask framework in order to demonstrate the products to the user. To verify the quantitative results, we evaluated our system using Area under Curve (AUC) and accuracy. The resulting experiments showed an average prediction AUC of 81% for the public UCF Crime dataset. The demo of our web-app anomaly detector can be found here https://youtu.be/X8iVyxkzsxA. © 2024 IEEE.
Universitas Syiah Kuala, Dept. of Electrical and Computer Engineering, Indonesia; Nodeflux, Indonesia; National Central University, Dept. of Mechanical Engineering, Taiwan
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