Feature Detector and Descriptor Evaluation in Human Action Recognition

Ling Shao, Riccardo Mattivi

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

58 Citations (Scopus)


In this paper, we evaluate and compare different feature detection and feature description methods for part-based approaches in human action recognition. Different methods have been proposed in the literature for both feature detection of space-time interest points and description of local video patches. It is however unclear which method performs better in the field of human action recognition. We compare, in the feature detection section, Dollar's method [18], Laptev's method [22], a bank of 3D-Gabor filters [6] and a method based on Space-Time Differences of Gaussians. We also compare and evaluate different descriptors such as Gradient [18], HOG-HOF [22], 3D SIFT [24] and an enhanced version of LBP-TOP [15]. We show the combination of Dollar's detection method and the improved LBP-TOP descriptor to be computationally efficient and to reach the best recognition accuracy on the KTH database.

Original languageEnglish
Title of host publication2010 ACM International Conference on Image and Video Retrieval
Number of pages8
Publication statusPublished - 2010
EventACM International Conference on Image and Video Retrieval, ACM-CIVR 2010 - Xi'an, China
Duration: 5 Jul 20107 Jul 2010


ConferenceACM International Conference on Image and Video Retrieval, ACM-CIVR 2010


  • Bag of words
  • Feature descriptors
  • Feature detectors
  • Human action recognition

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