The role of RGB-D benchmark datasets: an overview
By Kai Berger. 2013
10 citations recorded by the source corpus.
The advent of the Microsoft Kinect three years ago stimulated not only the computer vision community for new algorithms and setups to address well-known problems in the community but also sparked the launch of several new benchmark datasets to which future algorithms can be compared to. This review of the literature and industry developments concludes that the current RGB-D benchmark datasets can be useful to determine the accuracy of a variety of applications of a single or multiple RGB-D sensors.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Djamila Romaissa Beddiar
- Brahim Nini
- Abdenour Hadid
- Mohammad Sabokrou
- Michael Firman
- Yaojie Liu
- Ramachandra Raghavendra
- Amin Jourabloo
Citations in the corpus
- RGBD Datasets: Past, Present and Future (2016, methodology): “We are more comprehensive than earlier efforts, describing 101 datasets compared with the 14 in [9], 19 in [42]1 and the 44 action datasets in [115].”
- Vision-based human activity recognition: a survey (2020, background): “Similarly, authors in [20, 46, 180, 181, 232] present a set of comprehensive reviews of the most commonly used RGB-D video-based activity recognition datasets.”
- MobileRGBD, an open benchmark corpus for mobile RGB-D related algorithms (2014, unknown): “We totally agree with [9], benchmarking is one pillar of research in these domains: how can one states that an algorithm performs better without a common reference?”