• Corpus ID: 6386302

Spatio-temporal Video Parsing for Abnormality Detection

@article{Antic2015SpatiotemporalVP,
  title={Spatio-temporal Video Parsing for Abnormality Detection},
  author={Borislav Antic and Bj{\"o}rn Ommer},
  journal={ArXiv},
  year={2015},
  volume={abs/1502.06235}
}
Abnormality detection in video poses particular challenges due to the infinite size of the class of all irregular objects and behaviors. Thus no (or by far not enough) abnormal training samples are available and we need to find abnormalities in test data without actually knowing what they are. Nevertheless, the prevailing concept of the field is to directly search for individual abnormal local patches or image regions independent of another. To address this problem, we propose a method for… 
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References

SHOWING 1-10 OF 47 REFERENCES
Video parsing for abnormality detection
TLDR
A probabilistic model is presented that localizes abnormalities using statistical inference and outperforms the state-of-the-art to achieve a frame-based abnormality classification performance of 91% and the localization performance improves by 32% to 76%.
Detecting Irregularities in Images and in Video
  • Oren Boiman, M. Irani
  • Computer Science
    Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
  • 2005
TLDR
This work addresses the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images, using a probabilistic graphical model.
Video anomaly detection based on local statistical aggregates
TLDR
A key insight of the paper is that if anomalies are local optimal decision rules are local even when the nominal behavior exhibits global spatial and temporal statistical dependencies, this insight helps collapse the large ambient data dimension for detecting local anomalies.
Context-Aware Activity Recognition and Anomaly Detection in Video
TLDR
A mathematical framework to jointly model related activities with both motion and context information for activity recognition and anomaly detection and demonstrates the benefit of joint modeling and recognition of activities in a wide-area scene and the effectiveness of the proposed method in anomaly detection.
Anomaly Detection and Localization in Crowded Scenes
The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video
Learning object motion patterns for anomaly detection and improved object detection
TLDR
The proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback and successfully used the proposed scene model to detect local as well as global anomalies in object tracks.
Abnormality detection using low-level co-occurring events
Anomaly detection in crowded scenes
TLDR
A novel framework for anomaly detection in crowded scenes is presented and the proposed representation is shown to outperform various state of the art anomaly detection techniques.
Sparse reconstruction cost for abnormal event detection
TLDR
The method provides a unified solution to detect both local abnormal events and global abnormal events through a sparse reconstruction over the normal bases and extends it to support online abnormal event detection by updating the dictionary incrementally.
Detecting unusual activity in video
  • Hua Zhong, Jianbo Shi, M. Visontai
  • Computer Science
    Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
  • 2004
TLDR
It is proved that an efficient, globally optimal algorithm exists for the co- embedding problem and an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space.
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