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Anomalous Event Detection and Localization Using Stacked Autoencoder
2020Anomalous event detection and localization from the crowd is a challenging problem to the computer vision community. It is an important aspect of intelligent video surveillance. Surveillance cameras are set up to monitor anomalous or unusual events. But, the majority of video data, related to normal or usual events, is accessible.
Suprit D. Bansod, Abhijeet V. Nandedkar
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Intelligent Video Monitoring for Anomalous Event Detection
2011Behavior determination and multiple object tracking for video surveillance are two of the most active fields of computer vision. The reason for this activity is largely due to the fact that there are many application areas. This paper describes work in developing software algorithms for the tele-assistance for the elderly, which could be used as early ...
Iván Gómez Conde +3 more
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Anomalous Event Detection Methodologies for Surveillance Application
2017Automatic visual surveillance systems serve as in-place threat detection devices being able to detect and recognize anomalous activities which otherwise would lead to potentially harmful situations, and alert the concerned authorities to take appropriate counter actions.
T. J. Narendra Rao +3 more
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Use of sonification in the detection of anomalous events
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012, 2012In this paper, we describe the construction of a soundtrack that fuses stock market data with information taken from tweets. This soundtrack, or auditory display, presents the numerical and text data in such a way that anomalous events may be readily detected, even by untrained listeners.
Mark Ballora +5 more
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BATSense: Anomalous Security Event Detection using TBATS Machine Learning
2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), 2019The BATSense anomaly detection methodology has been running in a large university (~50k students) successfully detecting anomalies across 300k devices for a year. We use machine learning, specifically the TBATS forecasting algorithm, to predict future trends for the number of events per second for a variety of device types.
Pranshu Bajpai +4 more
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Anomalous Event Detection in Videos Using Supervised Classifier
2018Observing and modeling human behavior and activity patterns for detecting anomalous events has gained more attention in recent years, especially in the video surveillance system. An anomalous event is an event that differs from the normal or usual, but not necessarily in an undesirable manner.
K. Seemanthini, S. S. Manjunath
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Functional evaluation of an event detection ensemble to detect anomalous system behavior
Computers & Industrial Engineering, 2004Abstract Surveillance systems are established to manage the complexity of ensuring processes of interest behave as expected. They are formed in response to public demand that systems—natural and human-made—be predictable, managed, and under control.
Shaun M. Lynch, John R. Cook
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Detecting Anomalous Events on Distributed Systems Using Convolutional Neural Networks
2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 2019Detection of anomalous events is very crucial for the maintenance and performance tuning in long-running distributed systems. System logs contain the complete information of system operation that can be used for describing the situations of the computing nodes. However, log messages are unstructured and difficult to utilize.
Purimpat Cheansunan, Phond Phunchongharn
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Convolutional Sparse Coding-based Anomalous Event Detection in Surveillance Videos
2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2019This paper presents a Convolutional Sparse Coding (CSC)-based anomalous event detection method in surveillance videos. The proposed method derives new features from reconstruction errors and sparse coefficient maps obtained by CSC, and the anomalous events are detected by a multi-layer network whose inputs are the above new features. Since such events,
Masanao Matsumoto +3 more
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Adversarial autoencoders for anomalous event detection in images
2017Indiana University-Purdue University Indianapolis (IUPUI)
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