In recent years, video anomaly detection has been extensively investigated in
both unsupervised and weakly supervised settings to alleviate costly temporal
labeling. Despite significant progress, these methods still suffer from
unsatisfactory results such as numerous false alarms, primarily due to the
absence of precise temporal anomaly annotation. In this paper, we present a
novel labeling paradigm, termed "glance annotation", to achieve a better
balance between anomaly detection accuracy and annotation cost. Specifically,
glance annotation is a random frame within each abnormal event, which can be
easily accessed and is cost-effective. To assess its effectiveness, we manually
annotate the glance annotations for two standard video anomaly detection
datasets: UCF-Crime and XD-Violence. Additionally, we propose a customized
GlanceVAD method, that leverages gaussian kernels as the basic unit to compose
the temporal anomaly distribution, enabling the learning of diverse and robust
anomaly representations from the glance annotations. Through comprehensive
analysis and experiments, we verify that the proposed labeling paradigm can
achieve an excellent trade-off between annotation cost and model performance.
Extensive experimental results also demonstrate the effectiveness of our
GlanceVAD approach, which significantly outperforms existing advanced
unsupervised and weakly supervised methods. Code and annotations will be
publicly available at this https URL

本文提出了一种名为 “glance annotation” 的新型标注范式，通过在异常事件中添加随机帧来平衡异常检测准确性和标注成本，在 UCF-Crime 和 XD-Violence 两个标准视频异常检测数据集上进行了综合分析和实验验证，证明了该标注范式能够在标注成本和模型性能之间取得卓越的平衡，同时通过凭借高斯核作为基本单元来构建时序异常分布的 GlanceVAD 方法，在广泛的实验结果中明确了其在超越现有先进无监督和弱监督方法方面的有效性。