- Title
- An Evaluation of YOLO-Based Algorithms for Hand Detection in the Kitchen
- Creator
- Van Staden, Joshua
- Creator
- Brown, Dane L
- Subject
- To be catalogued
- Date Issued
- 2021
- Date
- 2021
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/465134
- Identifier
- vital:76576
- Identifier
- xlink:href="https://ieeexplore.ieee.org/abstract/document/9519307"
- Description
- Convolutional Neural Networks have offered an accurate method with which to run object detection on images. Specifically, the YOLO family of object detection algorithms have proven to be relatively fast and accurate. Since its inception, the different variants of this algorithm have been tested on different datasets. In this paper, we evaluate the performances of these algorithms on the recent Epic Kitchens-100 dataset. This dataset provides egocentric footage of people interacting with various objects in the kitchen. Most prominently shown in the footage is an egocentric view of the participants' hands. We aim to use the YOLOv3 algorithm to detect these hands within the footage provided in this dataset. In particular, we examine the YOLOv3 algorithm using two different backbones: MobileNet-lite and VGG16. We trained them on a mixture of samples from the Egohands and Epic Kitchens-100 datasets. In a separate experiment, average precision was measured on an unseen Epic Kitchens-100 subset. We found that the models are relatively simple and lead to lower scores on the Epic Kitchens 100 dataset. This is attributed to the high background noise on the Epic Kitchens 100 dataset. Nonetheless, the VGG16 architecture was found to have a higher Average Precision (AP) and is, therefore, more suited for retrospective analysis. None of the models was suitable for real-time analysis due to complex egocentric data.
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (6 pages)
- Format
- Publisher
- IEEE Xplore
- Language
- English
- Relation
- 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)
- Relation
- van Staden, J. and Brown, D., 2021, August. An Evaluation of YOLO-Based Algorithms for Hand Detection in the Kitchen. In 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp. 1-7). IEEE
- Relation
- 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) p. 1 2021
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the IEEE Xplore Terms of Use Statement (https://ieeexplore.ieee.org/Xplorehelp/overview-of-ieee-xplore/terms-of-use)
- Rights
- Closed Access
- Hits: 64
- Visitors: 64
- Downloads: 2
Collections
Van Staden, Joshua (Mr)
Brown, Dane A (Associate Prof)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | An Evaluation of YOLO-Based Algorithms for Hand Detection in the Kitchen.pdf | 646 KB | Adobe Acrobat PDF | View Details Download |