4 research outputs found
Detecting abnormal events in video using Narrowed Normality Clusters
We formulate the abnormal event detection problem as an outlier detection
task and we propose a two-stage algorithm based on k-means clustering and
one-class Support Vector Machines (SVM) to eliminate outliers. In the feature
extraction stage, we propose to augment spatio-temporal cubes with deep
appearance features extracted from the last convolutional layer of a
pre-trained neural network. After extracting motion and appearance features
from the training video containing only normal events, we apply k-means
clustering to find clusters representing different types of normal motion and
appearance features. In the first stage, we consider that clusters with fewer
samples (with respect to a given threshold) contain mostly outliers, and we
eliminate these clusters altogether. In the second stage, we shrink the borders
of the remaining clusters by training a one-class SVM model on each cluster. To
detected abnormal events in the test video, we analyze each test sample and
consider its maximum normality score provided by the trained one-class SVM
models, based on the intuition that a test sample can belong to only one
cluster of normality. If the test sample does not fit well in any narrowed
normality cluster, then it is labeled as abnormal. We compare our method with
several state-of-the-art methods on three benchmark data sets. The empirical
results indicate that our abnormal event detection framework can achieve better
results in most cases, while processing the test video in real-time at 24
frames per second on a single CPU.Comment: Accepted at WACV 2019. arXiv admin note: text overlap with
arXiv:1705.0818
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