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Description
Designing a Novel Multiphase Model for Superposition Prediction
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Comparing human action recognition and recognition from natural image datasets
Human action recognition is a fundamental challenge of many computer vision applications. In this paper, we propose a novel technique to learn the human action prediction capability of a machine-learning model. This approach uses a deep learning framework which learns a mapping from human action data. This data is composed of multiple instances representing multiple actions from a sequence of actions. By jointly learning a novel model, the two data instances with the human action data, we can use the feature vectors as a learning mechanism using a deep learning framework. We test the ability of our model to predict human actions using a wide variety of human action datasets. We found that our model outperformed human action recognition systems in accuracy on several datasets. The proposed model was very effective over human actions recognition task.
| https://zenodo.org/record/4584487 |
| https://zenodo.org/record/4584495 |
| https://zenodo.org/record/4584509 |
