Foreground object detection in highly dynamic scenes using saliency

Kai Hsiang Lin, Pooya Khorrami, Jiangping Wang, Mark Hasegawa-Johnson, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a novel saliency-based algorithm to detect foreground regions in highly dynamic scenes. We first convert input video frames to multiple patch-based feature maps. Then, we apply temporal saliency analysis to the pixels of each feature map. For each temporal set of co-located pixels, the feature distance of a point from its kth nearest neighbor is used to compute the temporal saliency. By computing and combining temporal saliency maps of different features, we obtain foreground likelihood maps. A simple segmentation method based on adaptive thresholding is applied to detect the foreground objects. We test our algorithm on images sequences of dynamic scenes, including public datasets and a new challenging wildlife dataset we constructed. The experimental results demonstrate the proposed algorithm achieves state-of-the-art results.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1125-1129
Number of pages5
ISBN (Electronic)9781479957514
DOIs
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Lin, K. H., Khorrami, P., Wang, J., Hasegawa-Johnson, M., & Huang, T. S. (2014). Foreground object detection in highly dynamic scenes using saliency. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 1125-1129). [7025224] (2014 IEEE International Conference on Image Processing, ICIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIP.2014.7025224