635 research outputs found
A Scene-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Abnormal event detection in video is a complex computer vision problem that
has attracted significant attention in recent years. The complexity of the task
arises from the commonly-agreed definition of an abnormal event, that is, a
rarely occurring event that typically depends on the surrounding context.
Following the standard formulation of abnormal event detection as outlier
detection, we propose a scene-agnostic framework that learns from training
videos containing only normal events. Our framework is composed of an object
detector, a set of appearance and motion auto-encoders, and a discriminator.
Since our framework only looks at object detections, it can be applied to
different scenes, provided that abnormal events are defined identically across
scenes. This makes our method scene agnostic, as we rely strictly on objects
that can cause anomalies, and not on the background. To overcome the lack of
abnormal data during training, we propose an adversarial learning strategy for
the auto-encoders. We create a scene-agnostic set of out-of-domain adversarial
examples, which are correctly reconstructed by the auto-encoders before
applying gradient ascent on the adversarial examples. We further utilize the
adversarial examples to serve as abnormal examples when training a binary
classifier to discriminate between normal and abnormal latent features and
reconstructions. Furthermore, to ensure that the auto-encoders focus only on
the main object inside each bounding box image, we introduce a branch that
learns to segment the main object. We compare our framework with the
state-of-the-art methods on three benchmark data sets, using various evaluation
metrics. Compared to existing methods, the empirical results indicate that our
approach achieves favorable performance on all data sets.Comment: Under revie
Predicting Next Local Appearance for Video Anomaly Detection
We present a local anomaly detection method in videos. As opposed to most
existing methods that are computationally expensive and are not very
generalizable across different video scenes, we propose an adversarial
framework that learns the temporal local appearance variations by predicting
the appearance of a normally behaving object in the next frame of a scene by
only relying on its current and past appearances. In the presence of an
abnormally behaving object, the reconstruction error between the real and the
predicted next appearance of that object indicates the likelihood of an
anomaly. Our method is competitive with the existing state-of-the-art while
being significantly faster for both training and inference and being better at
generalizing to unseen video scenes.Comment: Accepted as an oral presentation for MVA'202
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Anomaly detection is commonly pursued as a one-class classification problem,
where models can only learn from normal training samples, while being evaluated
on both normal and abnormal test samples. Among the successful approaches for
anomaly detection, a distinguished category of methods relies on predicting
masked information (e.g. patches, future frames, etc.) and leveraging the
reconstruction error with respect to the masked information as an abnormality
score. Different from related methods, we propose to integrate the
reconstruction-based functionality into a novel self-supervised predictive
architectural building block. The proposed self-supervised block is generic and
can easily be incorporated into various state-of-the-art anomaly detection
methods. Our block starts with a convolutional layer with dilated filters,
where the center area of the receptive field is masked. The resulting
activation maps are passed through a channel attention module. 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, providing empirical evidence that shows
considerable performance improvements on MVTec AD, Avenue, and ShanghaiTech. We
release our code as open source at https://github.com/ristea/sspcab.Comment: Accepted at CVPR 2022. Paper + supplementary (14 pages, 9 figures
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
Videos represent the primary source of information for surveillance
applications and are available in large amounts but in most cases contain
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.Comment: 15 pages, double colum
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
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