Learning Memory-Guided Normality for Anomaly Detection
Hyunjong Park,Jongyoun Noh,Bumsub Ham +2 more
- pp 14372-14381
TLDR
In this article, an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly, while lessening the representation capacity of CNNs is presented. But the main drawbacks of these approaches are that they do not consider the diversity this article.Abstract:
We address the problem of anomaly detection, that is, detecting anomalous events in a video sequence. Anomaly detection methods based on convolutional neural networks (CNNs) typically leverage proxy tasks, such as reconstructing input video frames, to learn models describing normality without seeing anomalous samples at training time, and quantify the extent of abnormalities using the reconstruction error at test time. The main drawbacks of these approaches are that they do not consider the diversity of normal patterns explicitly, and the powerful representation capacity of CNNs allows to reconstruct abnormal video frames. To address this problem, we present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly, while lessening the representation capacity of CNNs. To this end, we propose to use a memory module with a new update scheme where items in the memory record prototypical patterns of normal data. 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. Experimental results on standard benchmarks demonstrate the effectiveness and efficiency of our approach, which outperforms the state of the art.read more
Citations
More filters
Posted Content
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
Yong Shean Chong,Yong Haur Tay +1 more
TL;DR: In this article, a spatiotemporal architecture for anomaly detection in videos including crowded scenes is proposed, which includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features.
Proceedings ArticleDOI
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection
TL;DR: MeGA-CDA as mentioned in this paper employs category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features, and generates memory-guided category-specific attention maps which are then used to route the features appropriately to the corresponding category discriminator.
Journal ArticleDOI
A Survey of Single-Scene Video Anomaly Detection.
TL;DR: This article summarizes research trends on the topic of anomaly detection in video feeds of a single scene and categorizes and situates past research into an intuitive taxonomy, and provides a comprehensive comparison of the accuracy of many algorithms on standard test sets.
Posted Content
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Mariana-Iuliana Georgescu,Antonio Barbalau,Radu Tudor Ionescu,Fahad Shahbaz Khan,Marius Popescu,Mubarak Shah +5 more
TL;DR: This paper is the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture and outperforms the state-of-the-art methods on three benchmarks.
Proceedings ArticleDOI
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Mariana-Iuliana Georgescu,Antonio Barbalau,Radu Tudor Ionescu,Fahad Shahbaz Khan,Marius Popescu,Mubarak Shah +5 more
TL;DR: In this article, a 3D convolutional neural network is trained to produce discriminative anomaly-specific information by jointly learning multiple proxy tasks: three self-supervised and one based on knowledge distillation.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.