Leveraging Neuromorphic Computing for Human Action Detection With Deep Neural Networks

NMC(Neuro Morphic Computing) simulates and mimics the process of human brain operations using artificial neurons. The creation of human actions can be used to test and improve neuromorphic models, which are simulations of complex behaviours and interactions found in biological creatures. It can be contributed to the development of decision making capabilities in robots over time. The proposed system hence aims to use various deep learning models that train on UCF101 dataset to predict the set of actions or actions from given video clip. The proposed work aims to construct deep learning model, trained on large-scale datasets like UCF101, to efficiently learn and represent complex patterns and characteristics in human behaviors. Through the use of these models in the framework of neuromorphic computing, scientists can enhance the potential of artificial neural networks to more precisely describe and comprehend human behavior.

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