Human Activity Recognition
Ms. Shikha1, Rohan Kumar2, Shivam Aggarwal3, Shrey Jain4
1Ms. Shikha*, Assistant Professor, ECE Department, Delhi Technological University, Delhi.
2Rohan Kumar, B-tech, Electronics and Communication, Delhi Technological University, Delhi.
3Shivam Aggarwal, B-tech, Electronics and Communication, Delhi Technological University, Delhi. Shrey Jain , B-tech, Electronics and Communication, Delhi Technological University, Delhi.
Manuscript received on April 20, 2020. | Revised Manuscript received on April 30, 2020. | Manuscript published on May 10, 2020. | PP: 903-905 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5225059720/2020©BEIESP | DOI: 10.35940/ijitee.G5225.059720
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Convolutional neural networks (CNN), Human activities recognition (HAR), Kinetics dataset, Resnet.
Scope of the Article: Convolutional neural networks