Capturing the abnormal event from surveillance videos enhances the safety and
well-being of the citizens. The application of EdgeAI (Edge computing-based
Artificial Intelligent ) meets the strict latency requirements for security. In
this paper, we apply weakly supervised video anomaly detection called Robust
Temporal Feature Magnitude Learning (RTFM) to an end-to-end crime-scene anomaly
detection system from the surveillance cameras with the help of edge computing
technology. The system is tested directly on multiple Jetson edge devices
combined with TensorRT as the software developer kit from NVIDIA for system
performance enhancement. The experience of an AI-based system deployment on
various Jetson Edge devices with Docker technology is also provided. The
anomaly detection model yields competitive results compared to other
state-of-the-art (SOTA) algorithms on available datasets such as UCF-Crime and
UIT VNAnomaly. 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.

捕捉来自监控视频的异常事件提高了市民的安全和福祉。本文应用基于边缘计算的人工智能（EdgeAI）满足安全需求的低延迟要求。我们使用弱监督视频异常检测技术（RTFM）将端到端犯罪现场异常检测系统应用于监控摄像头，并借助边缘计算技术。该系统直接在多个 Jetson 边缘设备上进行测试，结合 NVIDIA 的软件开发工具包 TensorRT 以提高系统性能。该方法在可用数据集（如 UCF-Crime 和 UIT VNAnomaly）上与其他最先进算法相比取得了竞争力。该系统在使用仅 3.11GB RAM 的 Jetson 边缘设备上实现了每秒 47.56 帧的推理速度。我们还发现，该 AI 系统在能耗方面比早期版本的 Jetson 设备性能提高了 15%，而能耗降低了 50%。

在不同的 Jetson 边缘设备上对异常检测系统进行基准测试

Benchmarking Anomaly Detection System on various Jetson Edge Devices

Anomaly detection in videos refers to the identification of events that do
not conform to expected behavior. However, almost all existing methods tackle
the problem by minimizing the reconstruction errors of training data, which
cannot guarantee a larger reconstruction error for an abnormal event. In this
paper, we propose to tackle the anomaly detection problem within a video
prediction framework. To the best of our knowledge, this is the first work that
leverages the difference between a predicted future frame and its ground truth
to detect an abnormal event. To predict a future frame with higher quality for
normal events, other than the commonly used appearance (spatial) constraints on
intensity and gradient, we also introduce a motion (temporal) constraint in
video prediction by enforcing the optical flow between predicted frames and
ground truth frames to be consistent, and this is the first work that
introduces a temporal constraint into the video prediction task. Such spatial
and motion constraints facilitate the future frame prediction for normal
events, and consequently facilitate to identify those abnormal events that do
not conform the expectation. Extensive experiments on both a toy dataset and
some publicly available datasets validate the effectiveness of our method in
terms of robustness to the uncertainty in normal events and the sensitivity to
abnormal events.

该论文提出了一种在视频预测框架内解决异常检测问题的方法，利用预测未来画面与真实画面之间的差异来检测异常事件，并引入了空间和运动约束来促进正常事件的未来帧预测和识别不符合预期的异常事件。