Name
Abstract | ||
|---|---|---|
Recognizing human interactions is a challenging task due to partially occluded body parts and motion ambiguities in interactions. We observe that the interdependencies existing at both action level and body part level greatly help disambiguate similar individual movements and facilitate human interaction recognition. In this paper, we propose a novel hierarchical model to capture such interdependencies for recognizing interactions of two persons. We model the action of each person by a large-scale global feature and several body part features. Two types of contextual information are exploited in our model to capture the implicit and complex interdependencies between interaction class, the action classes of two persons and the labels of persons' body parts. We build a challenging human interaction dataset to test our method. Results show that our model is quite effective in recognizing human interactions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICME.2012.67 | ICME |
Keywords | DocType | ISSN |
video signal processing,human interaction dataset,body part,contextual information,body part feature,image recognition,novel hierarchical model,body part level,human interaction,human interaction recognition,action recognition,conditional random fields,occluded body part,action class,action level,hierarchical model,challenging human interaction dataset,semantics,optical imaging,hidden markov models,context modeling | Conference | 1945-7871 |
ISBN | Citations | PageRank |
978-1-4673-1659-0 | 5 | 0.42 |
References | Authors | |
13 | 2 | |