Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/479203
Title: Hidden part models for human action recognition: Probabilistic versus max margin
Authors: Wang, Yang;Mori, Greg
subject: Human action recognition;discriminative learning;hidden conditional random field;max margin;part-based model
Year: 2011
Abstract: 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.
Description: 
URI: http://localhost/handle/Hannan/284094
http://localhost/handle/Hannan/479203
ISSN: 2009070453
Appears in Collections:2011

Files in This Item:
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AL1935802.pdf1.04 MBAdobe PDF
Title: Hidden part models for human action recognition: Probabilistic versus max margin
Authors: Wang, Yang;Mori, Greg
subject: Human action recognition;discriminative learning;hidden conditional random field;max margin;part-based model
Year: 2011
Abstract: 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.
Description: 
URI: http://localhost/handle/Hannan/284094
http://localhost/handle/Hannan/479203
ISSN: 2009070453
Appears in Collections:2011

Files in This Item:
File SizeFormat 
AL1935802.pdf1.04 MBAdobe PDF
Title: Hidden part models for human action recognition: Probabilistic versus max margin
Authors: Wang, Yang;Mori, Greg
subject: Human action recognition;discriminative learning;hidden conditional random field;max margin;part-based model
Year: 2011
Abstract: 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.
Description: 
URI: http://localhost/handle/Hannan/284094
http://localhost/handle/Hannan/479203
ISSN: 2009070453
Appears in Collections:2011

Files in This Item:
File SizeFormat 
AL1935802.pdf1.04 MBAdobe PDF