  Innovative enhancement in embedded system platforms, specifically hardware
accelerations, significantly influence the application of deep learning in
real-world scenarios. These innovations translate human labor efforts into
automated intelligent systems employed in various areas such as autonomous
driving, robotics, Internet-of-Things (IoT), and numerous other impactful
applications. NVIDIA's Jetson platform is one of the pioneers in offering
optimal performance regarding energy efficiency and throughput in the execution
of deep learning algorithms. Previously, most benchmarking analysis was based
on 2D images with a single deep learning model for each comparison result. In
this paper, we implement an end-to-end video-based crime-scene anomaly
detection system inputting from surveillance videos and the system is deployed
and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin
Nano). The comparison analysis includes the integration of Torch-TensorRT as a
software developer kit from NVIDIA for the model performance optimisation. The
system is built based on the PySlowfast open-source project from Facebook as
the coding template. The end-to-end system process comprises the videos from
camera, data preprocessing pipeline, feature extractor and the anomaly
detection. We provide the experience of an AI-based system deployment on
various Jetson Edge devices with Docker technology. Regarding anomaly
detectors, a weakly supervised video-based deep learning model called Robust
Temporal Feature Magnitude Learning (RTFM) is applied in the system. The
approach system reaches 47.56 frames per second (FPS) inference speed on a
Jetson edge device with only 3.11 GB RAM usage total. We also discover the
promising Jetson device that the AI system achieves 15% better performance than
the previous version of Jetson devices while consuming 50% less energy power.
