ML Anthology

Video Behaviour Profiling and Abnormality Detection Without Manual Labelling

Tao Xiang, Shaogang Gong
ICCV 2005 pp. 1238-1245
doi:10.1109/ICCV.2005.248 /iccv/2005/xiang2005iccv-video/

Abstract

A novel framework is developed for automatic behaviour profiling and abnormality sampling/detection without any manual labelling of the training dataset. Natural grouping of behaviour patterns is discovered through unsupervised model selection and feature selection on the eigenvectors of a normalised affinity matrix. Our experiments demonstrate that a behaviour model trained using an unlabelled dataset is superior to those trained using the same but labelled dataset in detecting abnormality from an unseen video.

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Cite

Text

Xiang and Gong. "Video Behaviour Profiling and Abnormality Detection Without Manual Labelling." IEEE/CVF International Conference on Computer Vision, 2005. doi:10.1109/ICCV.2005.248

Markdown

[Xiang and Gong. "Video Behaviour Profiling and Abnormality Detection Without Manual Labelling." IEEE/CVF International Conference on Computer Vision, 2005.](https://mlanthology.org/iccv/2005/xiang2005iccv-video/) doi:10.1109/ICCV.2005.248

BibTeX

@inproceedings{xiang2005iccv-video,
  title     = {{Video Behaviour Profiling and Abnormality Detection Without Manual Labelling}},
  author    = {Xiang, Tao and Gong, Shaogang},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2005},
  pages     = {1238-1245},
  doi       = {10.1109/ICCV.2005.248},
  url       = {https://mlanthology.org/iccv/2005/xiang2005iccv-video/}
}