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