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ESR11: Henrique Piñeiro Monteagudo (Verizon Connect and UniBo)

  • ESR1: Patin Inkaew (University of Helsinki)
  • ESR5: Fotis Giasemis (Sorbonne University)
  • ESR7: James Gooding (TU Dortmund)
  • ESR8: Micol Olocco (TU Dortmund)
  • ESR6: Daniel Magdalinski (VU Amsterdam)
  • ESR2: Laura Boggia (IBM and Sorbonne University)
  • ESR3: Leon Bozianu (University of Geneva)
  • ESR4: Sofia Cella (CERN and University of Geneva)
  • ESR11: Henrique Piñeiro Monteagudo (Verizon Connect and UniBo)
  • ESR12: Pratik Jawahar (University of Manchester)
  • ESR10: Joachim Carlo Kristian Hansen (Lund University)
  • ESR9: Carlos Cocha (University of Heidelberg)
Home Early Stage Researchers ESR11: Henrique Piñeiro Monteagudo (Verizon Connect and UniBo)

ESR11: Real-Time Analysis through computer vision on dashcams and triggers in High Energy Physics, Verizon Connect in Florence and University of Bologna

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Project Description

The PhD student within this project will be trained in state-of-the-art computer vision algorithms based on deep learning and will study how to adapt and specialize them for real-time analysis of videos and sensor data collected by dashcams (camera on vehicle) installed on VERIZON customer vehicles. The objective of this project will be the development and field testing of algorithms to compute high level semantic understanding of road scenes suitable for resource-constrained platforms, like the Qualcomm Snapdragon 888, Ambarella CV25, or Raspberry Pi 3 by using frameworks like Tensorflow Lite, CVFlow or PyTorch Live. Example applications of interest are danger anticipation, driver drowsiness detection and surveillance, while example tasks to be optimized are detection of road objects and their position in space or sensor fusion of video, inertial measurements and GPS. ESR11 will propose simplifications of existing models to make them meet the real-time and low power constraints on embedded devices and will test their accuracy on publicly available datasets as well as real customer data. Collaboration and a stay in Sorbonne will provide training on heterogeneous computing architectures and optimal resource allocation, which will then be applied to the domain of this project. During a stay and collaboration at the University of Manchester, this PhD student will use simulated datasets within the Open Data Project to deliver a starting point for deep learning techniques in HEP triggers, where the initial testing ground will be using energy deposits left in the detector by hadronic jets as images. This work will also have connections with the project of the PhD student based in Helsinki for identification of jets from heavy quarks.

Host country: Italy
Host beneficiary: Verizon Connect Italy, Florence
PhD-awarding institution:  University of Bologna
Experiment affiliation: Industry-based / inter-experiment
Planned collaborations and secondments: Sorbonne, University of Manchester

ESR: Henrique Piñeiro Monteagudo

I am an automation engineer passionate about computer vision research. My research interests are in deep learning for computer vision applications, specially in resource-constrained scenarios. During the SMARTHEP project I hope we can produce some significant work in this field and its synergies with other real-time analysis topics while learning and having fun.

Offer

A PhD student in Verizon is a permanent, full time employee with the main duty to be engaged in PhD studies according to the study plan. The duration of PhD studies is 3 years full time, including a competitive salary, family and mobility and travel/training allowance. At the end of the PhD the person will join one of the R&D squads in Verizon Connect.

Institute and supervisor information

Verizon Connect operates in the field of Connected Vehicles, providing SaaS management solutions for fleets of commercial vehicles and assets. By fitting their fleet with Verizon Connect solutions, our customers achieve safer driving, reduced fuel used and increased productivity. The Italian branch of Verizon Connect includes a sales and R&D office in Ferrara and a research center in Florence, where the ESR will be hosted. UNIBO is the second largest university in Italy and one of the most active in research and technology transfer. It stands among the most important institutions of higher education in the EU with 87,000 enrolled students and 1,606 PhDs. At UNIBO, the ESR will be hosted within the Computer Vision lab (CVLab), which is a research laboratory active in the field of computer vision for more than 20 years.

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Main supervisor: Francesco Sambo

Francesco Sambo is Chief Scientist at Verizon Connect, where he leads internal AI/ML research and external collaborations with academia. His main research interest is the extraction of advanced road scene semantics from video and sensor data generated by connected vehicles.

Email Francesco

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Co-supervisor: Samuele Salti

Samuele Salti is currently associate professor at the Department of Computer Science and Engineering (DISI) of the University of Bologna, Italy.

His main research interest is computer vision, in particular 3D computer vision, and machine/deep learning applied to computer vision problems. He co-founded the spin-off eyecan.ai.

Contact information

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Co-supervisor: Leonardo Taccari

Leonardo Taccari is Lead Scientist at Verizon Connect, where he leads internal AI/ML research & development. His main research interest is machine learning and computer vision on sensor data from connected vehicles.

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ABOUT US

SMARTHEP is a network that connects the fields of High Energy Physics (HEP) and Data Science, especially in relation to the challenges of  processing large datasets using real-time analysis.

SMARTHEP is intended as a consortium formed by academic and industrial partners on scientific, technological, and entrepreneurship aspects of both HEP and Data Science.

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  • 21 January, 2018
    Comments Off on RAPID Workshop – October 2018

    RAPID Workshop – October 2018

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Email: smarthep-recruitment@cern.ch

 

SMARTHEP is funded by the European Union’s Horizon 2020 research and innovation programme, call H2020-MSCA-ITN-2020, under Grant Agreement n. 956086

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