Fast and Accurate Camera Scene Detection on Smartphones
- Resource Type
- Authors
- Maximilian Giang; Angeline Pouget; Toni Tanner; Ramithan Chandrapalan; Sidharth Ramesh; Radu Timofte; Moritz Prussing; Andrey Ignatov
- Source
- CVPR Workshops
- Subject
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Deep learning
Image and Video Processing (eess.IV)
Computer Science - Computer Vision and Pattern Recognition
Electrical Engineering and Systems Science - Image and Video Processing
Machine Learning (cs.LG)
Task (project management)
Mode (computer interface)
Robustness (computer science)
Research community
FOS: Electrical engineering, electronic engineering, information engineering
Computer vision
Artificial intelligence
business
- Language
- English
AI-powered automatic camera scene detection mode is nowadays available in nearly any modern smartphone, though the problem of accurate scene prediction has not yet been addressed by the research community. This paper for the first time carefully defines this problem and proposes a novel Camera Scene Detection Dataset (CamSDD) containing more than 11K manually crawled images belonging to 30 different scene categories. We propose an efficient and NPU-friendly CNN model for this task that demonstrates a top-3 accuracy of 99.5% on this dataset and achieves more than 200 FPS on the recent mobile SoCs. An additional in-the-wild evaluation of the obtained solution is performed to analyze its performance and limitation in the real-world scenarios. The dataset and pre-trained models used in this paper are available on the project website.