Skip to Main content Skip to Navigation
New interface
Journal articles

Activity representation with motion hierarchies

Adrien Gaidon 1, 2, 3, * Zaid Harchaoui 1 Cordelia Schmid 1 
* Corresponding author
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
3 Computer Vision
Xerox Research Centre Europe [Meylan]
Abstract : Complex activities, e.g., pole vaulting, are composed of a variable number of sub-events connected by complex spatio-temporal relations, whereas simple actions can be represented as sequences of short temporal parts. In this paper, we learn hierarchical representations of activity videos in an unsupervised manner. These hierarchies of mid-level motion components are data-driven decompositions specific to each video. We introduce a spectral divisive clustering algorithm to efficiently extract a hierarchy over a large number of tracklets (i.e., local trajectories). We use this structure to represent a video as an unordered binary tree. We model this tree using nested histograms of local motion features. We provide an efficient positive definite kernel that computes the structural and visual similarity of two hierarchical decompositions by relying on models of their parent-child relations. We present experimental results on four recent challenging benchmarks: the High Five dataset [Patron-Perez et al, 2010], the Olympics Sports dataset [Niebles et al, 2010], the Hollywood 2 dataset [Marszalek et al, 2009], and the HMDB dataset [Kuehne et al, 2011]. We show that pervideo hierarchies provide additional information for activity recognition. Our approach improves over unstructured activity models, baselines using other motion decomposition algorithms, and the state of the art.
Document type :
Journal articles
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download
Contributor : THOTH Team Connect in order to contact the contributor
Submitted on : Monday, November 25, 2013 - 9:17:13 AM
Last modification on : Saturday, November 19, 2022 - 3:59:00 AM
Long-term archiving on: : Wednesday, February 26, 2014 - 4:24:27 AM


Files produced by the author(s)



Adrien Gaidon, Zaid Harchaoui, Cordelia Schmid. Activity representation with motion hierarchies. International Journal of Computer Vision, 2014, 107 (3), pp.219-238. ⟨10.1007/s11263-013-0677-1⟩. ⟨hal-00908581⟩



Record views


Files downloads