Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies

• URL: http://arxiv.org/abs/2103.10502v1
• Date: Thu, 18 Mar 2021 20:08:53 GMT
• Title: Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies
• Authors: Masoud Pourreza, Mohammadreza Salehi, Mohammad Sabokrou
• Abstract summary: Video detection has proved to be a challenging task owing to its unsupervised training procedure and high-temporal existing in real-world scenarios. We propose a novel yet efficient method named Ano-Graph for learning and modeling the interaction of normal objects. Our method is data-efficient, significantly more robust against common real-world variations such as illumination, and passes SOTA by a large margin on the challenging datasets ADOC and Street Scene.
• Score: 11.935112157324122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection has proved to be a challenging task owing to its unsupervised training procedure and high spatio-temporal complexity existing in real-world scenarios. In the absence of anomalous training samples, state-of-the-art methods try to extract features that fully grasp normal behaviors in both space and time domains using different approaches such as autoencoders, or generative adversarial networks. However, these approaches completely ignore or, by using the ability of deep networks in the hierarchical modeling, poorly model the spatio-temporal interactions that exist between objects. To address this issue, we propose a novel yet efficient method named Ano-Graph for learning and modeling the interaction of normal objects. Towards this end, a Spatio-Temporal Graph (STG) is made by considering each node as an object's feature extracted from a real-time off-the-shelf object detector, and edges are made based on their interactions. After that, a self-supervised learning method is employed on the STG in such a way that encapsulates interactions in a semantic space. Our method is data-efficient, significantly more robust against common real-world variations such as illumination, and passes SOTA by a large margin on the challenging datasets ADOC and Street Scene while stays competitive on Avenue, ShanghaiTech, and UCSD.

Related papers

• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel spatial- temporal memories-enhanced graph autoencoder (STRIPE) STRIPE significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• 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)
• Video Anomaly Detection using GAN [0.0]
  This thesis study aims to offer the solution for this use case so that human resources won't be required to keep an eye out for any unusual activity in the surveillance system records. We have developed a novel generative adversarial network (GAN) based anomaly detection model.
  arXiv  Detail & Related papers  (2023-11-23T16:41:30Z)
• Automated Spatio-Temporal Graph Contrastive Learning [18.245433428868775]
  We develop an automated-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the heterogeneous graph with multi-view semantics well preserved. Experiments for three downstream-temporal mining tasks on several real-world datasets demonstrate the significant performance gain.
  arXiv  Detail & Related papers  (2023-05-06T03:52:33Z)
• Neural-Sim: Learning to Generate Training Data with NeRF [31.81496344354997]
  We present the first fully differentiable synthetic data pipeline that uses Neural Radiance Fields (NeRFs) in a closed-loop with a target application's loss function. Our approach generates data on-demand, with no human labor, to maximize accuracy for a target task.
  arXiv  Detail & Related papers  (2022-07-22T22:48:33Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting [75.10017445699532]
  Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem. We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
  arXiv  Detail & Related papers  (2021-11-25T08:45:14Z)
• Learning to Track with Object Permanence [61.36492084090744]
  We introduce an end-to-end trainable approach for joint object detection and tracking. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets.
  arXiv  Detail & Related papers  (2021-03-26T04:43:04Z)
• Graph Convolutional Networks for traffic anomaly [4.172516437934823]
  Event detection has been an important task in transportation, whose task is to detect points in time when large events disrupts a large portion of the urban traffic network. To fully capture the spatial and temporal traffic patterns remains a challenge, yet serves a crucial role for effective anomaly detection. We formulate the problem in a novel way, as detecting anomalies in a set of directed weighted graphs representing the traffic conditions at each time interval.
  arXiv  Detail & Related papers  (2020-12-25T22:36:22Z)
• Unsupervised Domain Adaptation with Temporal-Consistent Self-Training for 3D Hand-Object Joint Reconstruction [131.34795312667026]
  We introduce an effective approach to addressing this challenge by exploiting 3D geometric constraints within a cycle generative adversarial network (CycleGAN) In contrast to most existing works, we propose to enforce short- and long-term temporal consistency to fine-tune the domain-adapted model in a self-supervised fashion. We will demonstrate that our approach outperforms state-of-the-art 3D hand-object joint reconstruction methods on three widely-used benchmarks.
  arXiv  Detail & Related papers  (2020-12-21T11:27:56Z)
• A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction [74.00750936752418]
  We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC) First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
  arXiv  Detail & Related papers  (2020-03-13T04:35:50Z)

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.