Mutually incoherent pose bases for Action recognition
| Autor: | Wenbin Chen, Yinzhong Qian, I-Fan Shen |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science Pattern recognition 02 engineering and technology Pascal (programming language) 010501 environmental sciences Torso 01 natural sciences medicine.anatomical_structure 0202 electrical engineering electronic engineering information engineering medicine Action recognition 020201 artificial intelligence & image processing Computer vision Artificial intelligence business computer 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr.2016.7899737 |
| Popis: | We propose mutually incoherent pose bases for action recognition in static image, each of which implicitly represents co-occurrence of poselets. First of all, action specific poselets are trained. To suppress the ambiguity of detection, we cluster poselet activations by the overlap of predicted torso bound of each poselet. Then pose feature of an action person can be extracted which is a vector composed of poselet detection. In dictionary training, our challenge is that dictionary is over complete thus small perturbation in pose feature would cause significant change in sparse code, which might change classification result. In our framework, a penalty which induces pose bases become mutually incoherent is added to the objective function. We evaluate the method on PASCAL VOC 2012 Action dataset and Ikizler 5-Action dataset, experiment results show wonderful performance compared with counterparts and baselines. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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