Abnormal Event Detection In Videos Using Deep Embedding

• URL: http://arxiv.org/abs/2409.09804v1
• Date: Sun, 15 Sep 2024 17:44:51 GMT
• Title: Abnormal Event Detection In Videos Using Deep Embedding
• Authors: Darshan Venkatrayappa,
• Abstract summary: Abnormal event detection or anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose an unsupervised approach for video anomaly detection with the aim to jointly optimize the objectives of the deep neural network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Abnormal event detection or anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without supervision. In this work we propose an unsupervised approach for video anomaly detection with the aim to jointly optimize the objectives of the deep neural network and the anomaly detection task using a hybrid architecture. Initially, a convolutional autoencoder is pre-trained in an unsupervised manner with a fusion of depth, motion and appearance features. In the second step, we utilize the encoder part of the pre-trained autoencoder and extract the embeddings of the fused input. Now, we jointly train/ fine tune the encoder to map the embeddings to a hypercenter. Thus, embeddings of normal data fall near the hypercenter, whereas embeddings of anomalous data fall far away from the hypercenter.

Related papers

• 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)
• 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)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• Anomaly Detection with Adversarially Learned Perturbations of Latent Space [9.473040033926264]
  Anomaly detection is to identify samples that do not conform to the distribution of the normal data. In this work, we have designed an adversarial framework consisting of two competing components, an Adversarial Distorter, and an Autoencoder. The proposed method outperforms the existing state-of-the-art methods in anomaly detection on image and video datasets.
  arXiv  Detail & Related papers  (2022-07-03T19:32:00Z)
• Visual anomaly detection in video by variational autoencoder [0.0]
  An autoencoder is a neural network that is trained to recreate its input using latent representation of input also called a bottleneck layer. In this paper we have demonstrated comparison between performance of convolutional LSTM versus a variation convolutional LSTM autoencoder.
  arXiv  Detail & Related papers  (2022-03-08T06:22:04Z)
• UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection [103.06327681038304]
  We propose a supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time. We show that UBnormal can enhance the performance of a state-of-the-art anomaly detection framework.
  arXiv  Detail & Related papers  (2021-11-16T17:28:46Z)
• DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection [9.19194451963411]
  Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. We propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation.
  arXiv  Detail & Related papers  (2021-06-09T21:57:41Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning [25.787860059872106]
  Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. We propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset.
  arXiv  Detail & Related papers  (2021-04-15T06:41:23Z)
• 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)
• Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection [114.9714355807607]
  We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods. We devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data.
  arXiv  Detail & Related papers  (2020-03-15T08:44:55Z)

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.