Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows

• URL: http://arxiv.org/abs/2404.17381v1
• Date: Fri, 26 Apr 2024 12:56:16 GMT
• Title: Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows
• Authors: Shun Maeda, Chunzhi Gu, Jun Yu, Shogo Tokai, Shangce Gao, Chao Zhang,
• Abstract summary: We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner. Compared to prior human-related anomaly detection tasks which primarily focus on unusual events from videos, HAAD involves the learning of specific action labels to recognize semantically anomalous human behaviors.
• Score: 13.77542608443034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of training action samples. Compared to prior human-related anomaly detection tasks which primarily focus on unusual events from videos, HAAD involves the learning of specific action labels to recognize semantically anomalous human behaviors. To address this task, we propose a normalizing flow (NF)-based detection framework where the sample likelihood is effectively leveraged to indicate anomalies. As action anomalies often occur in some specific body parts, in addition to the full-body action feature learning, we incorporate extra encoding streams into our framework for a finer modeling of body subsets. Our framework is thus multi-level to jointly discover global and local motion anomalies. Furthermore, to show awareness of the potentially jittery data during recording, we resort to discrete cosine transformation by converting the action samples from the temporal to the frequency domain to mitigate the issue of data instability. Extensive experimental results on two human action datasets demonstrate that our method outperforms the baselines formed by adapting state-of-the-art human activity AD approaches to our task of HAAD.

Related papers

• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD. It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring [2.394081903745099]
  We propose MeLIAD, a novel methodology for interpretable anomaly detection. MeLIAD is based on metric learning and achieves interpretability by design without relying on any prior distribution assumptions of true anomalies. Experiments on five public benchmark datasets, including quantitative and qualitative evaluation of interpretability, demonstrate that MeLIAD achieves improved anomaly detection and localization performance.
  arXiv  Detail & Related papers  (2024-09-20T16:01:43Z)
• Enhancing Anomaly Detection via Generating Diversified and Hard-to-distinguish Synthetic Anomalies [7.021105583098609]
  Recent approaches have focused on leveraging domain-specific transformations or perturbations to generate synthetic anomalies from normal samples. We introduce a novel domain-agnostic method that employs a set of conditional perturbators and a discriminator. We demonstrate the superiority of our method over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2024-09-16T08:15:23Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Abnormal Event Detection via Hypergraph Contrastive Learning [54.80429341415227]
  Abnormal event detection plays an important role in many real applications. In this paper, we study the unsupervised abnormal event detection problem in Attributed Heterogeneous Information Network. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns.
  arXiv  Detail & Related papers  (2023-04-02T08:23:20Z)
• Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Features [3.255030588361124]
  Unsupervised anomaly action recognition identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples. We present a unified, user prompt-guided zero-shot learning framework using a target domain-independent skeleton feature extractor. We incorporate a similarity score between the user prompt embeddings and skeleton features aligned in the common space into the anomaly score, which indirectly supplements normal actions.
  arXiv  Detail & Related papers  (2023-03-27T12:59:33Z)
• Object-centric and memory-guided normality reconstruction for video anomaly detection [56.64792194894702]
  This paper addresses anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy. Our model learns object-centric normal patterns without seeing anomalous samples during training.
  arXiv  Detail & Related papers  (2022-03-07T19:28:39Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• Understanding the Effect of Bias in Deep Anomaly Detection [15.83398707988473]
  Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection.
  arXiv  Detail & Related papers  (2021-05-16T03:55:02Z)
• A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video [120.18562044084678]
  Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. We propose a background-agnostic framework that learns from training videos containing only normal events.
  arXiv  Detail & Related papers  (2020-08-27T18:39:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.