Video classification using transfer learning techniques for human-machine interface

Neha K.; Sai Charan P.; Sridhar V.

doi:10.1117/12.2593834

1 August 2021 Video classification using transfer learning techniques for human-machine interface

Neha K., Sai Charan P., Sridhar V.

Proceedings Volume 11843, Applications of Machine Learning 2021; 118430X (2021) https://doi.org/10.1117/12.2593834
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Conference Poster

Abstract

Video classification is a crucial aspect when we discuss human-machine interface as it helps to analyze various activities. Using transfer learning techniques can help us in making predictions accurately. The dataset used for research is a subdivision of the UCF101-Action Recognition Dataset, consisting of 10 classes in total, where each class contains more than 120 videos. Each video is converted into a series of frames at a frame rate of 5. Feature extraction is performed on these frames using InceptionV3. The fine-tuned model architecture is composed of 4 dense layers. These layers are built using “relu” activation function with 1024, 512, 256 and 128 neurons respectively and another dense layer is built using “softmax” activation function with 10 neurons so as to predict 10 classes. This technique finds a huge range of applications related to human-machine interface such as helping the visually challenged people in classifying various activities.

Citation Download Citation

Neha K., Sai Charan P., and Sridhar V. "Video classification using transfer learning techniques for human-machine interface", Proc. SPIE 11843, Applications of Machine Learning 2021, 118430X (1 August 2021); https://doi.org/10.1117/12.2593834

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