Simultaneous segmentation and classification of human actions in video streams using deeply optimized Hough transform
| Autor: | Adrien Chan-Hon-Tong, Catherine Achard, Laurent Lucat |
|---|---|
| Přispěvatelé: | Département Intelligence Ambiante et Systèmes Interactifs (DIASI), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)) |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Computer science
02 engineering and technology Human actions Temporal domain Hough transform law.invention Activity recognition [SPI]Engineering Sciences [physics] Artificial Intelligence law 020204 information systems 0202 electrical engineering electronic engineering information engineering Temporal localization Segmentation Computer vision Video streaming Image segmentation Classification (of information) business.industry Hough transforms Pattern recognition Image recognition Pipeline (software) ComputingMethodologies_PATTERNRECOGNITION Signal Processing 020201 artificial intelligence & image processing State-of-the-art performance Computer Vision and Pattern Recognition Artificial intelligence Human activity recognition business Software Hough |
| Zdroj: | Pattern Recognition Pattern Recognition, Elsevier, 2014, 47, pp.3807-3818. ⟨10.1016/j.patcog.2014.05.010⟩ Pattern Recognition, 2014, 47, pp.3807-3818. ⟨10.1016/j.patcog.2014.05.010⟩ |
| ISSN: | 0031-3203 |
| Popis: | International audience; Most researches on human activity recognition do not take into account the temporal localization of actions. In this paper, a new method is designed to model both actions and their temporal domains. This method is based on a new Hough method which outperforms previous published ones on honeybee dataset thanks to a deeper optimization of the Hough variables. Experiments are performed to select skeleton features adapted to this method and relevant to capture human actions. With these features, our pipeline improves state-of-the-art performances on TUM dataset and outperforms baselines on several public datasets. |
| Databáze: | OpenAIRE |
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