Version 1
: Received: 25 August 2019 / Approved: 28 August 2019 / Online: 28 August 2019 (03:52:22 CEST)
How to cite:
Shamsipour, G.; Pirasteh, S. Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone. Preprints2019, 2019080289. https://doi.org/10.20944/preprints201908.0289.v1
Shamsipour, G.; Pirasteh, S. Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone. Preprints 2019, 2019080289. https://doi.org/10.20944/preprints201908.0289.v1
Shamsipour, G.; Pirasteh, S. Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone. Preprints2019, 2019080289. https://doi.org/10.20944/preprints201908.0289.v1
APA Style
Shamsipour, G., & Pirasteh, S. (2019). Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone. Preprints. https://doi.org/10.20944/preprints201908.0289.v1
Chicago/Turabian Style
Shamsipour, G. and Saied Pirasteh. 2019 "Artificial Intelligence and Convolutional Neural Network for Recognition of Human Interaction by Video from Drone" Preprints. https://doi.org/10.20944/preprints201908.0289.v1
Abstract
Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.
Keywords
drone video; human action recognition; CNN; Support vector machine (SVM)
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
