A Review of Anomaly Detection in Automated Surveillance

@article{Sodemann2012ARO,
  title={A Review of Anomaly Detection in Automated Surveillance},
  author={Angela A. Sodemann and Matthew P. Ross and Brett J. Borghetti},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)},
  year={2012},
  volume={42},
  pages={1257-1272}
}
As surveillance becomes ubiquitous, the amount of data to be processed grows along with the demand for manpower to interpret the data. A key goal of surveillance is to detect behaviors that can be considered anomalous. As a result, an extensive body of research in automated surveillance has been developed, often with the goal of automatic detection of anomalies. Research into anomaly detection in automated surveillance covers a wide range of domains, employing a vast array of techniques. This… Expand

Figures, Tables, and Topics from this paper

A survey of video datasets for anomaly detection in automated surveillance
  • N. Patil, P. Biswas
  • Geography, Computer Science
  • 2016 Sixth International Symposium on Embedded Computing and System Design (ISED)
  • 2016
TLDR
A survey on anomaly detection video datasets is necessary for researchers to understand and progress in this domain and various datasets description briefly is presented. Expand
Anomaly Detection using Edge Computing in Video Surveillance System: Review
TLDR
A systematic categorization of methodologies developed to detect anomalies in intelligent video surveillance using edge devices and approaches explicitly designed for them is presented and the challenges and opportunities involved in anomaly detection at the edge are discussed. Expand
Dynamic clustering for event detection and anomaly identification in video surveillance
TLDR
From the analysis presented in this work, it is evident that a more comprehensive analysis, closely following human perception can be accomplished by incorporating the proposed notions and algorithms in a video surveillance event. Expand
Algorithms for anomaly detection in video sequences through discriminative models
TLDR
The state of the art included in this work has been developed to make a complete study of all these aspects in detail, as well as a study of advantages and drawbacks of the main methods of the literature, helping to choose the best techniques and strategies for specific surveillance scenarios. Expand
A Review On Anomaly Detection In Video Surveillance Systems
The cameras are widely used nowadays, have contributed to the development of automatic surveillance systems. With surveillance systems, interest of studies to detection of unusual situations in videoExpand
Machine Learning for Anomaly Detection: A Systematic Review
TLDR
A Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application and provides researchers with recommendations and guidelines based on this review. Expand
Energy-based Models for Video Anomaly Detection
TLDR
This work proposes to work with regular patterns whose unlabeled data is abundant and usually easy to collect in practice, which allows the system to be trained completely in an unsupervised procedure and liberate the author from the need for costly data annotation. Expand
Deep Abnormality Detection in Video Data
  • H. Vu
  • Computer Science
  • IJCAI
  • 2017
TLDR
This work proposes a deep abnormality detection system that is an unsupervised probabilistic framework to model the normality and learn feature representation automatically and can detect abnormality at multiple levels and be used as a powerful tool for video analysis and scene understanding. Expand
Energy-Based Localized Anomaly Detection in Video Surveillance
TLDR
A unified framework for anomaly detection in video based on the restricted Boltzmann machine, a recent powerful method for unsupervised learning and representation learning, that can detect and localize the abnormalities at pixel level with better accuracy than those of baselines, and achieve competitive performance compared with state-of-the-art approaches. Expand
Detecting anomalous activities by fusion of accelerometer and passive infrared sensor
TLDR
The experimental results over real-life data show the effectiveness of the combination of an accelerometer and PIR sensor approach in detecting anomalous activities and therefore potentially reducing the use of cameras. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 106 REFERENCES
Event Detection in Automated Surveillance Systems
TLDR
In this paper, hidden Markov models (HMM) are utilized to recognize the nature of an event occurring in a scene and a novel approach is proposed in which the clues provided by centroid clustering are utilized. Expand
Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems
TLDR
The behavior analysis module of the OBSERVER, a video surveillance system that detects and predicts abnormal behaviors aiming at the intelligent surveillance concept, is presented, where the DOG method outperforms the previously used N-ary trees classifier. Expand
Video Behavior Profiling for Anomaly Detection
  • T. Xiang, S. Gong
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2008
TLDR
It is shown that a behavior model trained using an unlabeled data set is superior to those trained using the same but labeled data set in detecting anomaly from an unseen video, and the online LRT-based behavior recognition approach is advantageous over the commonly used Maximum Likelihood method in differentiating ambiguities among different behavior classes observed online. Expand
Estimation of crowd behavior using sensor networks and sensor fusion
TLDR
This article presents an approach to automatically interpret sensor data and estimate behaviors of groups of people in order to provide the operator with relevant warnings, using data from distributed heterogeneous sensors and the use of radars for weapon detection. Expand
Automatic detection of anomalous behavioural events for advanced real-time video surveillance
TLDR
The proposed system is capable of automatically adapting to different scenarios without any human intervention (but the placement of the TV sensors), and uses robust self-learning techniques to automatically learn the "typical" behaviour of the targets in each specific operative environment. Expand
Abnormal behavior-detection using sequential syntactical classification in a network of clustered cameras
TLDR
This work uses an architecture for a network of clustered cameras to minimize and efficiently manage bandwidth utilization and uses the infrastructure outputs per cluster, per person, to detect abnormal behaviors intra-cluster. Expand
Anomaly detection: A survey
TLDR
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique. Expand
Toward Credible Evaluation of Anomaly-Based Intrusion-Detection Methods
TLDR
The current state of the experimental practice in the area of anomaly-based intrusion detection is reviewed and 276 studies in this area published during the period of 2000-2008 are reviewed and the common pitfalls among surveyed works are identified. Expand
Local Abnormality Detection in Video Using Subspace Learning
TLDR
Experimental resultson a real underground station dataset shows that the linear approach is better suited for cases where the subspacelearning is restricted to the labeled samples, whereas the then-linear approach is preferable in the presence of additionalunlabeled data. Expand
The OBSERVER: An Intelligent and Automated Video Surveillance System
TLDR
The OBSERVER is capable of identifying three types of behaviors (normal, unusual and abnormal actions) and was possible due to the novel N-ary tree classifier, which was successfully tested on synthetic data. Expand
...
1
2
3
4
5
...