ACTION RECOGNITION USING SALIENT NEIGHBORING HISTOGRAMS

Huamin Ren, Thomas B. Moeslund

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

3 Citations (Scopus)
182 Downloads (Pure)

Abstract

Combining spatio-temporal interest points with Bag-of-Words models achieves state-of-the-art performance in action recognition. However, existing methods based on “bag-ofwords” models either are too local to capture the variance in space/time or fail to solve the ambiguity problem in spatial and temporal dimensions. Instead, we propose a salient vocabulary construction algorithm to select visual words from a global point of view, and form compact descriptors to represent discriminative histograms in the neighborhoods. Those salient neighboring histograms are then trained to model different actions. Our approach yields a competitive result on the KTH dataset compare to state-of-the-art methods. On the more challenging UCF Sports dataset, we obtain 95.21%, which is approximately 4% better than the current best published results.
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Number of pages5
PublisherIEEE Signal Processing Society
Publication date2013
Pages2807-2811
ISBN (Print)978-1-4799-2341-0
Publication statusPublished - 2013
EventICIP 2013: The International Conference on Image Processing 2013 (ICIP) - Melbourne, Australia
Duration: 15 Sep 201318 Sep 2013

Conference

ConferenceICIP 2013
CountryAustralia
CityMelbourne
Period15/09/201318/09/2013

Keywords

  • Salient visual words, neighboring histograms, action recognition

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