PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An evaluation of bags-of-words and spatio-temporal shapes for action recognition
Teo de Campos, Mark Barnard, Krystian Mikolajczyk, Josef Kittler, Fei Yan, William Christmas and David Windridge
In: IEEE Workshop on Applications of Computer Vision (WACV), 05-06 Jan 2011, Kona, Hawaii.

Abstract

Bags-of-visual-Words (BoW) and Spatio-Temporal Shapes (STS) are two very popular approaches for action recognition from video. The former (BoW) is an un-structured global representation of videos which is built using a large set of local features. The latter (STS) uses a single feature located on a region of interest (where the actor is) in the video. Despite the popularity of these methods, no comparison between them has been done. Also, given that BoW and STS differ intrinsically in terms of context inclusion and globality/locality of operation, an appropriate evaluation framework has to be designed carefully. This paper compares these two approaches using four different datasets with varied degree of space-time specificity of the actions and varied relevance of the contextual background. We use the same local feature extraction method and the same classifier for both approaches. Further to BoW and STS, we also evaluated novel variations of BoW constrained in time or space. We observe that the STS approach leads to better results in all datasets whose background is of little relevance to action classification.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:7029
Deposited By:Teo de Campos
Deposited On:03 December 2010