IEEE Trans Pattern Anal Mach Intell - Animated Pose Templates for Modelling and Detecting Human Actions.

Tópicos

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Resumo

This paper presents Animated Pose Templates for detecting short-term, long-term and contextual actions from cluttered scenes in videos. Each pose template consists of two components: i) a shape template whose appearances represented by the Histogram of Oriented Gradient features; and ii) a motion template using the Histogram of Optical Flow features. While this pose template is suitable for detecting short-term action snippets, we extend it in two ways: i) for long-term actions, we animate the pose templates by adding temporal constraints in a Hidden Markov Model; and ii) for contextual actions, we treat contextual objects as additional parts of the pose templates. To train the model, we manually annotate part locations on some key frames, then introduce a Semi-Supervised Structural SVM algorithm that iterates between: i) learning model parameters from labeled data by solving a structural SVM optimization; and 2) imputing latent variables on unannotated frames and progressively accepting high score ones as newly labelled examples. The inference algorithm has two steps: i) Detecting top candidates for the pose templates; and ii) Computing the sequence of pose templates. Both are done by dynamic programming. In experiments, we test our method on both public and our own datasets. The results show that our model achieves comparable or better performance than state-of-the-art.

Resumo Limpo

paper present anim pose templat detect shortterm longterm contextu action clutter scene video pose templat consist two compon shape templat whose appear repres histogram orient gradient featur ii motion templat use histogram optic flow featur pose templat suitabl detect shortterm action snippet extend two way longterm action anim pose templat ad tempor constraint hidden markov model ii contextu action treat contextu object addit part pose templat train model manual annot part locat key frame introduc semisupervis structur svm algorithm iter learn model paramet label data solv structur svm optim imput latent variabl unannot frame progress accept high score one newli label exampl infer algorithm two step detect top candid pose templat ii comput sequenc pose templat done dynam program experi test method public dataset result show model achiev compar better perform stateoftheart

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