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 https://github.com/TeCSAR-UNCC/CHAD.

翻译：近年来，基于数据驱动的深度学习方法在视频异常检测领域引起了广泛关注，该类方法需判定视频特定帧中是否包含异常行为。然而，视频异常检测具有显著的场景依赖性，代表性数据集的可用性严重制约了真实场景下的检测精度。此外，当前大多数最先进方法所报告的指标往往无法反映模型在真实场景中的实际表现。本文提出夏洛特异常数据集（CHAD）。CHAD是一个面向商业停车场场景的高分辨率多视角异常数据集。除帧级异常标签外，CHAD是首个为每个目标提供边界框、身份标识及姿态标注的异常数据集。这对基于骨架的异常检测尤为有利——因其在真实场景中具有更低的计算需求。CHAD亦是首个包含同一场景多视角图像的异常数据集。该数据集包含四个摄像头视角、超过115万帧图像，是当前规模最大、包含行人标注的完全标注异常检测数据集，数据采集自固定摄像头连续视频流，适用于智能视频监控应用。为验证CHAD在训练与评估中的有效性，我们在该数据集上对两种基于骨架的最新异常检测算法进行了基准测试，并通过定量结果与定性分析相结合的方式提供了全面评估。数据集访问地址：https://github.com/TeCSAR-UNCC/CHAD。