Open AccessHuman Activity Recognition
Author(s) -
Ms. Shikha*,
Rajan Kumar,
Shivam Aggarwal,
Shrey Jain
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g5225.059720
Subject(s) - activity recognition , computer science , convolutional neural network , artificial intelligence , accelerometer , field (mathematics) , set (abstract data type) , gyroscope , machine learning , pattern recognition (psychology) , computer vision , engineering , mathematics , pure mathematics , programming language , aerospace engineering , operating system
The topic of Human activity recognition (HAR) is a prominent research area topic in the field of computer vision and image processing area. It has empowered state-of-art application in multiple sectors, surveillance, digital entertainment and medical healthcare. It is interesting to observe and intriguing to predict such kind of movements. Several sensor-based approaches have also been introduced to study and predict human activities such accelerometer, gyroscope, etc., it has its own advantages and disadvantages.[10] In this paper, an intelligent human activity recognition system is developed. Convolutional neural network (CNN) with spatiotemporal three dimensional (3D) kernels are trained using Kinetics data set which has 400 classes that depicts activities of humans in their everyday life and work and consist of 400 and more videos for each class. The 3D CNN model used in this model is RESNET-34. The videos were temporally cut down and last around tenth of a second. The trained model show satisfactory performance in all stages of training, testing. Finally the results show promising activity recognition of over 400 human actions.