Abstract
Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
Highlights
Unsupervised representation learning has become an important domain with the advent of deep generative models which include the variational autoencoder (VAE) [1], generative adversarial networks (GANs) [2], Long Short Term memory networks (LSTMs) [3], and others
Representation learning for reconstruction: Methods such as Principal component analysis (PCA), Autoencoders (AEs) are used to represent the different linear and non-linear transformations to the appearance or motion, that model the normal behavior in surveillance videos
An important common aspect in all these models is the problem of representation learning, which refers to the feature extraction or transformation of input training data for the task of anomaly detection
Summary
Unsupervised representation learning has become an important domain with the advent of deep generative models which include the variational autoencoder (VAE) [1] , generative adversarial networks (GANs) [2], Long Short Term memory networks (LSTMs) [3] , and others. Anomalies are rarely annotated and labeled data rarely available to train a deep convolutional network to separate normal class from the anomalous class This is a fairly complex task since the class of normal points includes frequently occurring objects and regular foreground movements while the anomalous class include various types of rare events and unseen objects that could be summarized as a consistent class. Given a set of training samples containing no anomalies, the goal of anomaly detection is to design or learn a feature representation, that captures “normal” motion and spatial appearance patterns. Any deviations from this normal can be identified by measuring the approximation error either geometrically in a vector space or the posterior probability of a given model which fits training. Imaging 2018, 4, 36 sample representation vectors or by modeling the conditional probability of future samples given their past values and measuring the prediction error of test samples by training a predictive model, accounting for temporal structure in videos
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
