Harnessing Convolutional Neural Networks and Transfer Learning to Perform Vision-Oriented Activity Recognition of Humans
Keywords:
Human Activity Recognition, Criminological Investigations, Convolutional Neural Networks, Transfer Learning.Abstract
Since the debut of the Internet of Things, there has always been a lot of noteworthy evolution in the aspect of "Human Activity recognition". Recognition of activities of humans possesses its own connotation and purpose and can be employed in diverse range of disciplines featuring medical assistance, nefarious activities, and espionage. It's possible that it could be critically pertinent in order to undergo ample amount of criminological investigations. To anticipate various human behaviors, a myriad of machine learning techniques are used. However, deep learning models have trounced standard machine learning strategies. Convolutional Neural Networks (CNN), a type of deep learning model, could very well heuristically extract the features and drastically cut overall processing expenditure. The action recognition kinetics dataset can be used to predict human activities using the CNN model. Here, we use transfer learning specifically for visual categorization problems.
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