IEEE Trans Pattern Anal Mach Intell - Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin.

Tópicos

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Resumo

We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (HCRF) for object recognition. Similarly to HCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Differently from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying HCRF on local patches alone. We also propose an alternative for learning the parameters of an HCRF model in a max-margin framework. We call this method the max-margin hidden conditional random field (MMHCRF). We demonstrate that MMHCRF outperforms HCRF in human action recognition. In addition, MMHCRF can handle a much broader range of complex hidden structures arising in various problems in computer vision.

Resumo Limpo

present discrimin partbas approach human action recognit video sequenc use motion featur model base recent propos hidden condit random field hcrf object recognit similar hcrf object recognit model human action flexibl constel part condit imag observ differ object recognit model combin largescal global featur local patch featur distinguish various action experiment result show model compar stateoftheart approach action recognit particular experiment result demonstr combin largescal global featur local patch featur perform signific better direct appli hcrf local patch alon also propos altern learn paramet hcrf model maxmargin framework call method maxmargin hidden condit random field mmhcrf demonstr mmhcrf outperform hcrf human action recognit addit mmhcrf can handl much broader rang complex hidden structur aris various problem comput vision

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