Recent advancements in artificial intelligence (AI) have seen the emergence
of smart video surveillance (SVS) in many practical applications, particularly
for building safer and more secure communities in our urban environments.
Cognitive tasks, such as identifying objects, recognizing actions, and
detecting anomalous behaviors, can produce data capable of providing valuable
insights to the community through statistical and analytical tools. However,
artificially intelligent surveillance systems design requires special
considerations for ethical challenges and concerns. The use and storage of
personally identifiable information (PII) commonly pose an increased risk to
personal privacy. To address these issues, this paper identifies the privacy
concerns and requirements needed to address when designing AI-enabled smart
video surveillance. Further, we propose the first end-to-end AI-enabled
privacy-preserving smart video surveillance system that holistically combines
computer vision analytics, statistical data analytics, cloud-native services,
and end-user applications. Finally, we propose quantitative and qualitative
metrics to evaluate intelligent video surveillance systems. The system shows
the 17.8 frame-per-second (FPS) processing in extreme video scenes. However,
considering privacy in designing such a system results in preferring the
pose-based algorithm to the pixel-based one. This choice resulted in dropping
accuracy in both action and anomaly detection tasks. The results drop from
97.48 to 73.72 in anomaly detection and 96 to 83.07 in the action detection
task. On average, the latency of the end-to-end system is 36.1 seconds.

本文提出了一种终端到终端的人工智能视频安全监控系统，其中融合了计算机视觉分析、统计数据分析、云原生服务以及终端用户应用，该系统能够更好地保护个人隐私，同时对人物识别、错误行为检测等方面具有高效性能和较高的安全性。

了解 AI-enabled 智能视频监控的政策和技术方面以解决公共安全问题

Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety

In recent years, we have seen a significant interest in data-driven deep
learning approaches for video anomaly detection, where an algorithm must
determine if specific frames of a video contain abnormal behaviors. However,
video anomaly detection is particularly context-specific, and the availability
of representative datasets heavily limits real-world accuracy. Additionally,
the metrics currently reported by most state-of-the-art methods often do not
reflect how well the model will perform in real-world scenarios. In this
article, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a
high-resolution, multi-camera anomaly dataset in a commercial parking lot
setting. In addition to frame-level anomaly labels, CHAD is the first anomaly
dataset to include bounding box, identity, and pose annotations for each actor.
This is especially beneficial for skeleton-based anomaly detection, which is
useful for its lower computational demand in real-world settings. CHAD is also
the first anomaly dataset to contain multiple views of the same scene. With
four camera views and over 1.15 million frames, CHAD is the largest fully
annotated anomaly detection dataset including person annotations, collected
from continuous video streams from stationary cameras for smart video
surveillance applications. To demonstrate the efficacy of CHAD for training and
evaluation, we benchmark two state-of-the-art skeleton-based anomaly detection
algorithms on CHAD and provide comprehensive analysis, including both
quantitative results and qualitative examination. The dataset is available at
this https URL.

文章介绍了一种用于视频异常检测的深度学习算法，提出了一个高分辨率、多摄像头商业停车场场景异常数据集 CHAD，并在其中评估了两种最新方法的效力，为智能视频监控应用提供了有用参考。