PhD Student in Cooperative Tracking and Visual Analytics Ghent University, Department of Telecommunications and information processing

PhD student in Cooperative tracking and visual analytics


Last application date Dec 31, 2017 00:00

DepartmentTW07 - Department of Telecommunications and information processing

Contract Limited duration

Degree Master of Science degree (at the start of the PhD) and a background in Electrical Engineering or Computer Science

Occupancy rate 100%

Vacancy Type Research staff

 

Job description

 

For the Innovative Training Network (ITN) project ACHIEVE - AdvanCed Hardware/Software components for Integrated/Embedded Vision systEms (H2020-MSCA-ITN-2017) we are looking for two motivated early stage researchers in the field of cooperative multi-camera computer vision, specifically on cooperative tracking, visual analytics and scene modeling. The researcher fellow will be hosted at the Image Processing and Interpretation (IPI) research group of Ghent University's Faculty of Engineering and Architecture, Fac. of Sciences, for a period of 36 months with the aim of obtaining a PhD. The PhD training includes a secondment (internship) in the company FLIR (http://www.flir.com), a producer of smart cameras. FLIR is located in Kortrijk, nearby the city of Ghent. Cooperative vision is new paradigm for multi-camera computer vision that combines embedded video processing in smart cameras with an advanced inter-camera messaging system to implement system wide computer vision algorithms, i.e., algorithms which run on networks of tens over even hundreds of cameras. The main additional challenges compared to traditional computer vision relate to the need for system-level reasoning and analysis, and to achieve real-time operation using an optimal balance between local and global processing. IPI has a long standing expertise in multi-camera and real-time computer vision and has several projects in this general area. The new researchers will therefore work as a team with existing researchers. They will also cooperate with the other researchers in the ACHIEVE network and participate in the ACHIEVE's training program.

 

Our offer

 

You will receive a PhD scholarship according to the general conditions at Ghent University. The tax free scholarship includes full social security coverage (net monthly amount starting at ± 1.900 EUR/month + 250 EUR/month mobility allowance + (if applicable) family allowance of 500 EUR). The initial contract will be for a period of 1 year and will start in the first quarter of 2018; this contract can be extended for a total of 4 years, subject to good performance. You will work at the IPI research group. Information about IPI can be found on the web: http://ipi.ugent.be

 

Objectives

 

- One of the most common but still challenging requirements in multi-camera video processing is the ability to automatically track objects over multiple cameras. In intelligent traffic management, for example, objects of interest include not only vehicles but also weak road users. Current state of the art approaches focus either on feature-modeling that designs descriptors invariant to camera changes or on metric learning that often require prohibitive amount of training data. Vehicle tracking/re-identification is equally challenging in difficult circumstances. The first goal of this project is to design algorithms for distributed multiple targets tracking through a decentralized approach. The second goal is to improve object detection and tracking using a multi-sensor approach. Thermal cameras have promising potential in surveillance applications, especially when combined with optical cameras. The third goal of the project is to provide solutions for behavior analysis and action recognition. The research will use high-level analysis to automatically determine which cameras observe the same or similar action, such as pedestrians waiting to cross the street. Deep learning is a promising approach.

 

Profile of the candidate

 

You have a Master of Science degree (at the start of the PhD) and a background in Electrical Engineering or Computer Science. Candidates with an MsC in another discipline but with a strong knowledge of mathematics, signal/image processing, machine learning and/or probability theory may also be considered.

 

You have a strong interest in video processing, a good knowledge of mathematics, signal or image processing, and basic programming skills. In our lab we use C++, Quasar (a new language for rapid development on smart cameras) and Python. The research work will involve creating new mathematical models and algorithms for cooperative computer vision, producing real-time implementations and evaluating these on real-life data.

 

You function well in a team. You have good or excellent English and scientific writing skills. You combine a strong interest in scientific research with a desire to see your work applied in industry. Due to EC funding rules, only candidates with less than 4 years of research experience can be considered. Candidates may not have carried out their main activity (work studies ...) in Belgium for more than 12 months in the past 3 years. UGent implements gender neutral recruitment and selection procedures. Female candidates are especially encouraged to apply.

 

How to apply

 

Please submit your application by email to both Prof. Wilfried Philips at Wilfried.Philips@UGent.be and Dr. Ljiljana Platisa at Ljiljana.Platisa@UGent.be.

 

In your email, please include the following:

  • A brief motivation of your application: what do you consider the best facts in your CV which demonstrate your academic excellence in BsC and/or Msc. education? What are your reasons to pursue a PhD. Why would you like to work at UGent? ...

  • A detailed CV, describing your earlier experience and studies;

  • A list of publications (if available);

  • A transcript of your educational record (list of courses per year, number of obtained credits, obtained marks) if available. This need not be official document at this stage;

  • A (rough) indication or estimate of your rank among other students (e.g., top 10% among 35 students in my master);

  • If available: 1-3 English language documents describing your earlier research (e.g., scientific papers, master thesis, report on project work, etc.). These documents need not be on the topic of the positions.


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