Related papers: Object-centric and memory-guided normality reconstruction for video anomaly detection

Object-centric and memory-guided normality reconstruction for video anomaly detection

URL: http://arxiv.org/abs/2203.03677v4
Date: Fri, 19 May 2023 09:21:10 GMT
Title: Object-centric and memory-guided normality reconstruction for video anomaly detection
Authors: Khalil Bergaoui, Yassine Naji, Aleksandr Setkov, Ang\'elique Loesch, Mich\`ele Gouiff\`es and Romaric Audigier
Abstract summary: 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.
Score: 56.64792194894702
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper addresses video anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy, in which our model learns object-centric normal patterns without seeing anomalous samples during training. The main contributions consist in coupling pretrained object-level action features prototypes with a cosine distance-based anomaly estimation function, therefore extending previous methods by introducing additional constraints to the mainstream reconstruction-based strategy. Our framework leverages both appearance and motion information to learn object-level behavior and captures prototypical patterns within a memory module. Experiments on several well-known datasets demonstrate the effectiveness of our method as it outperforms current state-of-the-art on most relevant spatio-temporal evaluation metrics.

Related papers

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)
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)
Open-Vocabulary Video Anomaly Detection [57.552523669351636]
Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
arXiv Detail & Related papers (2023-11-13T02:54:17Z)
Spatio-temporal predictive tasks for abnormal event detection in videos [60.02503434201552]
We propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
arXiv Detail & Related papers (2022-10-27T19:45:12Z)
Spatio-Temporal Relation Learning for Video Anomaly Detection [35.59510027883497]
Anomaly identification is highly dependent on the relationship between the object and the scene. In this paper, we propose a Spatial-Temporal Relation Learning framework to tackle the video anomaly detection task. Experiments are conducted on three public datasets, and the superior performance over the state-of-the-art methods demonstrates the effectiveness of our method.
arXiv Detail & Related papers (2022-09-27T02:19:31Z)
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection [97.93062818228015]
We propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video.
arXiv Detail & Related papers (2021-11-17T13:30:31Z)
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)
Learning Memory-guided Normality for Anomaly Detection [33.77435699029528]
We present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly. We also present novel feature compactness and separateness losses to train the memory, boosting the discriminative power of both memory items and deeply learned features from normal data.
arXiv Detail & Related papers (2020-03-30T05:30:09Z)

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