1. Unsupervised Video Anomaly Detection 无监督异常检测依赖于well-defined distribution of normal events 的假设。实际上正常的行为也是具有多种变型,训练集是否足以概括了所有的情况?模型是否完善描述了正常的分布?前者决定了无监督学习方法的上限,后者决定了模型性能的下限。 绝大多数方法默认了前者是足够的。(当然简...
视频异常检测(Video Anomaly Detection VAD)因为数据特殊性,主流方法都是采用无监督、弱监督的方式进行,辅助以多任务、自监督等方法,都取得了不错的成果。但是对于无监督的定义一直都是很模糊的,很多论文将训练时只采用正常数据的方法称为无监督,但是正常数据本身就是一种标签,更像是异常点检测问题,之前投稿的论文也...
Video anomaly detection has always been a challenging task in computer vision due to data imbalance and susceptibility to scene variations such as lighting and occlusions. In response to this challenge, this paper proposes an unsupervised video anomaly detection method based on an attention-enhanced ...
Video anomaly detection is commonly used in many applications such as security surveillance and is very challenging. A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences bet...
Video anomaly detection plays an increasingly crucial role in intelligent surveillance systems. Inspired by previous unsupervised methods, this paper focuses on detecting frame-level anomalies with long-term temporal dependencies. To this end, we propose a dual-scale temporal dependency learning method for...
the effectiveness of the unsupervised video anomaly detection methods mostly requires the large size of video dataset and massivecomputational resources. With the ease of availability of these two entities,unsupervised techniqueshave been outperforming the supervised video anomaly detection methods. In contras...
(2016). Once their model was trained, both studies produced a “regularity score” based on the reconstruction error, highlighting that video sequences with regular events had low errors. In contrast, anomalous sequences had high errors, allowing them to spot anomalies. 2.4.2 Anomaly Detection ...
Video Anomaly Detection (VAD) has been extensively studied under the settings of One-Class Classification (OCC) and Weakly-Supervised learning (WS), which however both require laborious human-annotated normal/abnormal labels. In this paper, we study Unsupervised VAD (UVAD) that does not depend on...
几篇论文实现代码:《Unsupervised Model Selection for Time-series Anomaly Detection》(ICLR 2023) GitHub: github.com/mononitogoswami/tsad-model-selection [fig10] 《CLIPood: Generalizing CLIP to Out-...
这篇论文提出了一种新的基线方法,用于在保护隐私的协作学习环境中进行无监督视频异常检测(Unsupervised Video Anomaly Detection, US-VAD)。该方法名为CLAP(Collaborative Learning of Anomalies with Privacy),能够在不需要任何标签的完全无监督方式下,定位复杂监控视频中的异常事件。此外,论文还提出了三种新的评估协议,...