Abstract

Approaches to abnormality detection in crowded scene largely rely on supervised methods using discriminative models. In this paper, we presents a novel and efficient unsupervised learning method for video analysis. We start from visual saliency, which has been used in several vision tasks, e.g., image classification, object detection, and foreground segmentation. To detect saliency regions in video sequences, we propose a new approach for detecting spatiotemporal visual saliency based on the phase spectrum of the videos, which is easy to implement and computationally efficient. With the proposed algorithm, we also study how the spatiotemporal saliency can be used in two important vision tasks, saliency prediction and abnormality detection. The proposed algorithm is evaluated on several benchmark datasets with comparison to the state-of-the-art methods from the literature. The experiments demonstrate the effectiveness of the proposed approach to spatiotemporal visual saliency detection and its application to the above vision tasks.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Other

OtherIEEE Winter Conference on Applications of Computer Vision, WACV 2016
CountryUnited States
CityLake Placid
Period3/7/163/10/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Wang, Y., Zhang, Q., & Li, B. (2016). Efficient unsupervised abnormal crowd activity detection based on a spatiotemporal saliency detector. In 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 [7477684] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2016.7477684