Abstract
Unsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity recently due to its practical real-world applications. As surveillance videos are privacy sensitive and the availability of large-scale video data may enable better US-VAD systems, collaborative learning can be highly rewarding in this setting. However, due to the extremely challenging nature of the US-VAD task, where learning is carried out without any annotations, privacy-preserving collaborative learning of US-VAD systems has not been studied yet. In this paper, we propose a new baseline for anomaly detection capable of localizing anomalous events in complex surveillance videos in a fully unsupervised fashion without any labels on a privacy-preserving participant-based distributed training configuration. Additionally, we propose three new evaluation protocols to benchmark anomaly detection approaches on various scenarios of collaborations and data availability. Based on these protocols, we modify existing VAD datasets to extensively evaluate our approach as well as existing US SOTA methods on two large-scale datasets including UCF-Crime and XD-Violence. All proposed evaluation protocols, dataset splits, and codes are available here: this https URL
Abstract (translated)
近年来，由于其具有实际应用价值，无监督（US）视频异常检测（VAD）在监视领域越来越受到欢迎。由于监视视频对个人隐私敏感，并且大规模视频数据的可用性可能使更好的US-VAD系统更加成熟，在这种场景下，协作学习可能具有很高的价值。然而，由于US-VAD任务的极度困难性，其中学习过程没有标签，因此还没有研究过在保护参与者隐私的分布式训练配置下进行隐私保留的US-VAD系统的协作学习。在本文中，我们提出了一个能够在不进行任何标签的情况下，以完全无监督的方式定位复杂监视视频中的异常事件的新的基线。此外，我们还提出了三个新的评估协议，以评估各种协作场景下的异常检测方法。基于这些协议，我们对包括UCF-Crime和XD-Violence在内的两个大型数据集进行了广泛评估，以评估我们的方法和现有US SOTA方法的性能。所提出的评估协议、数据集拆分和代码都可以在这里找到：https:// this URL
URL
https://arxiv.org/abs/2404.00847
