Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental and adaptive abnormal behaviour detection
Computer Vision and Image Understanding
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
The evolution of video surveillance: an overview
Machine Vision and Applications
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Detecting abnormal human behaviour using multiple cameras
Signal Processing
Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Towards Generic Detection of Unusual Events in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
A survey on vision-based human action recognition
Image and Vision Computing
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Volumetric Features for Video Event Detection
International Journal of Computer Vision
Detecting and discriminating behavioural anomalies
Pattern Recognition
Dense spatio-temporal features for non-parametric anomaly detection and localization
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance
Image and Vision Computing
Anomalous video event detection using spatiotemporal context
Computer Vision and Image Understanding
Learning spatio-temporal dependency of local patches for complex motion segmentation
Computer Vision and Image Understanding
Action Recognition from One Example
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action Recognition Using Mined Hierarchical Compound Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting anomalies in people's trajectories using spectral graph analysis
Computer Vision and Image Understanding
Surveillance-Oriented Event Detection in Video Streams
IEEE Intelligent Systems
Human behavior clustering for anomaly detection
Frontiers of Computer Science in China
Recent advances and trends in visual tracking: A review
Neurocomputing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A suspicious behaviour detection using a context space model for smart surveillance systems
Computer Vision and Image Understanding
Multivalued default logic for identity maintenance in visual surveillance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Multi-scale and real-time non-parametric approach for anomaly detection and localization
Computer Vision and Image Understanding
Selective spatio-temporal interest points
Computer Vision and Image Understanding
Sparse reconstruction cost for abnormal event detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Online detection of unusual events in videos via dynamic sparse coding
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
International Journal of Computer Vision
Spatiotemporal Localization and Categorization of Human Actions in Unsegmented Image Sequences
IEEE Transactions on Image Processing
Trajectory-Based Anomalous Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
CRV '12 Proceedings of the 2012 Ninth Conference on Computer and Robot Vision
Video parsing for abnormality detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Online Dominant and Anomalous Behavior Detection in Videos
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
Editor's Choice Article: Human activity recognition in videos using a single example
Image and Vision Computing
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This paper presents an approach for detecting suspicious events in videos by using only the video itself as the training samples for valid behaviors. These salient events are obtained in real-time by detecting anomalous spatio-temporal regions in a densely sampled video. The method codes a video as a compact set of spatio-temporal volumes, while considering the uncertainty in the codebook construction. The spatio-temporal compositions of video volumes are modeled using a probabilistic framework, which calculates their likelihood of being normal in the video. This approach can be considered as an extension of the Bag of Video words (BOV) approaches, which represent a video as an order-less distribution of video volumes. The proposed method imposes spatial and temporal constraints on the video volumes so that an inference mechanism can estimate the probability density functions of their arrangements. Anomalous events are assumed to be video arrangements with very low frequency of occurrence. The algorithm is very fast and does not employ background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. Experiments were performed on four video datasets of abnormal activities in both crowded and non-crowded scenes and under difficult illumination conditions. The proposed method outperformed all other approaches based on BOV that do not account for contextual information.