The invention consists of a method for estimating depth, optical flow, and other semantic information on low-power devices. Specifically, low-resolution images acquired from a single camera are processed by a lightweight and highly accurate self-supervised Convolutional Neural Network.
| Patent title | Method for determining the depth from a single image and system thereof |
|---|---|
| Thematic area | Industry, Digital and Security |
| Ownership | ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA, POLITECNICO DI TORINO |
| Inventors | Valentino Peluso, Antonio Cipolletta, Andrea Calimera, Stefano Mattoccia, Fabio Tosi, Filippo Aleotti, Matteo Poggi |
| Protection | International (PCT) |
| Licensing status | Available for development agreements, option, license and other exploitation agreements |
| Keywords | Monocular Sensors, Low-Resolution Images, Low-Power Devices, Deep Neural Networks, Embedded Systems |
| Filed on | 24 March 2021 |
Estimating the depth and the optical flow from a scene is crucial in several computer vision applications. A recent trend aims to infer such cues from a single camera to simplify the setup and allow their use in application contexts characterized by severe cost and size constraints.
The invention consists of a tiny Convolutional Neural Network capable of processing low-resolution images to obtain coarse semantic information of the observed scene. The network can run on off-the-shelf microcontroller units with minimal power requirements (a few hundreds of mW). Nevertheless, it is accurate enough to serve as the backbone of many high-level IoT applications such as people tracking, simple traffic monitoring, and privacy-preserving monitoring systems. Moreover, the network is trained in a self-supervised manner; thus, it does not require costly ground-truth annotations during the training phase.
Application:
- Proximity control systems
- Tracking systems
- Traffic monitoring systems
- Privacy-preserving monitoring systems
- Augmented and Virtual Reality
Advantages:
- Extraction of dense semantic information from a single image
- Trained with self-supervised learning
- Compatible with mobile battery-powered devices
- Low cost