27,364 research outputs found
Visual Anomaly Detection in Event Sequence Data
Anomaly detection is a common analytical task that aims to identify rare
cases that differ from the typical cases that make up the majority of a
dataset. When applied to the analysis of event sequence data, the task of
anomaly detection can be complex because the sequential and temporal nature of
such data results in diverse definitions and flexible forms of anomalies. This,
in turn, increases the difficulty in interpreting detected anomalies. In this
paper, we propose an unsupervised anomaly detection algorithm based on
Variational AutoEncoders (VAE) to estimate underlying normal progressions for
each given sequence represented as occurrence probabilities of events along the
sequence progression. Events in violation of their occurrence probability are
identified as abnormal. We also introduce a visualization system, EventThread3,
to support interactive exploration and interpretations of anomalies within the
context of normal sequence progressions in the dataset through comprehensive
one-to-many sequence comparison. Finally, we quantitatively evaluate the
performance of our anomaly detection algorithm and demonstrate the
effectiveness of our system through a case study
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
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