Embedded vision is a disruptive new technology in the vision industry. It is a revolutionary concept with far reaching implications, and it is opening up new applications and shaping the future of entire industries. It is applied in self-driving cars, autonomous vehicles in agriculture, digital dermascopes that help specialists make more accurate diagnoses, among many other unique and cutting-edge applications. The design of such systems gives rise to new challenges for embedded Software developers. Embedded vision applications are characterized by stringent performance constraints to guarantee real-time behaviours and, at the same time, energy constraints to save battery on the mobile platforms. In this paper, we address such challenges by proposing an overall view of the problem and by analysing current solutions. We present our last results on embedded vision design automation over two main aspects: the adoption of the model-based paradigm for the embedded vision rapid prototyping, and the application of heterogeneous programming languages to improve the system performance. The paper presents our recent results on the design of a localization and mapping application combined with image recognition based on deep learning optimized for an NVIDIA Jetson TX2.

Rapid Prototyping of Embedded Vision Systems: Embedding Computer Vision Applications into Low-Power Heterogeneous Architectures

Stefano Aldegheri;Nicola Bombieri
2018

Abstract

Embedded vision is a disruptive new technology in the vision industry. It is a revolutionary concept with far reaching implications, and it is opening up new applications and shaping the future of entire industries. It is applied in self-driving cars, autonomous vehicles in agriculture, digital dermascopes that help specialists make more accurate diagnoses, among many other unique and cutting-edge applications. The design of such systems gives rise to new challenges for embedded Software developers. Embedded vision applications are characterized by stringent performance constraints to guarantee real-time behaviours and, at the same time, energy constraints to save battery on the mobile platforms. In this paper, we address such challenges by proposing an overall view of the problem and by analysing current solutions. We present our last results on embedded vision design automation over two main aspects: the adoption of the model-based paradigm for the embedded vision rapid prototyping, and the application of heterogeneous programming languages to improve the system performance. The paper presents our recent results on the design of a localization and mapping application combined with image recognition based on deep learning optimized for an NVIDIA Jetson TX2.
Embedded vision, Heterogeneous architectures, OpenVX, GPU, ORB-SLAM, Jetson TX2
File in questo prodotto:
File Dimensione Formato  
main.pdf

accesso aperto

Tipologia: Documento in Pre-print
Licenza: Creative commons
Dimensione 499.14 kB
Formato Adobe PDF
499.14 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/985819
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact