83 research outputs found
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
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
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
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model
Recently, intelligent video surveillance applications have become essential in public security by the use of computer vision technologies to investigate and understand long video streams. Anomaly detection and classification are considered a major element of intelligent video surveillance. The aim of anomaly detection is to automatically determine the existence of abnormalities in a short time period. Deep reinforcement learning (DRL) techniques can be employed for anomaly detection, which integrates the concepts of reinforcement learning and deep learning enabling the artificial agents in learning the knowledge and experience from actual data directly. With this motivation, this paper presents an Intelligent Video Anomaly Detection and Classification using Faster RCNN with Deep Reinforcement Learning Model, called IVADC-FDRL model. The presented IVADC-FDRL model operates on two major stages namely anomaly detection and classification. Firstly, Faster RCNN model is applied as an object detector with Residual Network as a baseline model, which detects the anomalies as objects. Besides, deep Q-learning (DQL) based DRL model is employed for the classification of detected anomalies. In order to validate the effective anomaly detection and classification performance of the IVADC-FDRL model, an extensive set of experimentations were carried out on the benchmark UCSD anomaly dataset. The experimental results showcased the better performance of the IVADC-FDRL model over the other compared methods with the maximum accuracy of 98.50% and 94.80% on the applied Test004 and Test007 dataset respectively
- …