MACHINE LEARNING FOR HUMAN RECOGNITION
DOI:
https://doi.org/10.17770/het2022.26.6956Keywords:
artificial neural network, COCO dataset, human recognition, YOLOv5Abstract
The aim of this work is to develop a neural network that will be able to recognize human presence. To achieve this goal, authors applied neural network architecture YOLOv5 and the open dataset COCO. The experiment was repeated three times. The study yielded a good result - the neural network was able to detect humans in images with a good precision.Downloads
References
Human detection and tracking with deep convolutional neural networks [tiešsaiste], [atsauce uz 01.05.2022]. Pieejams: https://link.springer.com/article/10.1007/s11042-020-09579-x
Konvolūcijas neironu tīkls [tiešsaiste], [atsauce uz 01.05.2022]. Pieejams: https://www.techtarget.com/searchenterpriseai/definition/convolutional-neural-network
YOLOv5: The Latest Model for Object Detection [tiešsaiste], [atsauce uz 01.05.2022]. Pieejams: https://medium.com/axinc-ai/yolov5-the-latest-model-for-object-detection-b13320ec516b
Comon Objects In Context [tiešsaiste], [atsauce uz 09.05.2022]. Pieejams: https://cocodataset.org/#download
FiftyOne [tiešsaiste], [atsauce uz 10.05.2022]. Pieejams: https://voxel51.com/fiftyone/index.html
Computer Vision Datasets [tiešsaiste], [atsauce uz 09.05.2022]. Pieejams: https://public.roboflow.com/
