Aggregating the temporal coherent descriptors in videos using multiple learning kernel for action recognition
Action recognition methods enable several intelligent machines to recognize human action in their daily life videos. Indeed, many action recognition methods give a noticeable misclassification rate due to the big variations within the videos of the same class, and the changes in viewpoint, scale and...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/692482 |
| Acceso en línea: | http://hdl.handle.net/10486/692482 https://dx.doi.org/10.1016/j.patrec.2017.06.010 |
| Access Level: | acceso abierto |
| Palabra clave: | Action recognition Representation learning Coherence analysis Learning-to-rank Multiple kernel learning Telecomunicaciones |
| Sumario: | Action recognition methods enable several intelligent machines to recognize human action in their daily life videos. Indeed, many action recognition methods give a noticeable misclassification rate due to the big variations within the videos of the same class, and the changes in viewpoint, scale and background. In this paper, we propose a new video representations method that captures temporal evolution of the action happening in the whole video. We show that 1) combining the descriptors of improved dense trajectories with a multiple kernel learning technique can reduce the misclassification rate, and also 2) aggregating the coherent frames in each video may have a different impact on the recognition results. Our experimental results using HMDB51 and Hollywood datasets demonstrate that our method is on par with the state-of-the-art methods |
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