• 1 Ph.D. position at Daimler R&D
• Various Masters Thesis positions (“Diplomarbeit”, “afstudeerscriptie”) at Daimler R&D or the University of Amsterdam
• Various student internships at Daimler R&D. Minimum duration is 5 months (no summer internships are offered).

Daimler
Pedestrian
Detection
Benchmark

Open Ph.D. Position, Daimler R&D (Ulm, Germany)

Active Pedestrian Safety

The Environment Perception Department of Daimler R&D in Ulm (Germany)  has currently an opening for a Ph.D. student in the area of computer vision and pattern recognition.

The open position concerns the development of a sensor-based driver assistance system that can detect dangerous traffic situations with pedestrians, in order to warn the driver or automatically control the vehicle. Research will focus on two sub-themes: a) how to reliably and accurately detect pedestrians by computer vision, and b) how to estimate collision risk by the use of motion models, learned from training data. Experiments will be performed both off-line, on unique large data sets, and on-board a Mercedes-Benz vehicle demonstrator, on the test track and in real urban traffic.

Qualifications

Prospective applicants should have an outstanding academic record and a solid background computer science or applied sciences. Experience in computer vision and pattern recognition is a definite plus. The successful applicant will furthermore have strong programming skills (e.g. C++/MATLAB) and a certain affinity towards turning complex techniques into real systems. The ability to speak (or willingness to learn) german is expected.

Availability

Funding for the Ph.D. student position is available immediately, for a period of 3-4 years. Academic affiliation will be arranged. Prospective applicants please email

• cover letter (explaining suitability)
• resume
• copy of academic record (courses/grades)
• supporting materials (e.g. MS Thesis, reports/publications, GRE/TOEFL scores)

to Prof. Dr. D.M. Gavrila

Positions at the Master Level
Daimler (Ulm, Germany) or University of Amsterdam (NL)

Here are a few possible topics:

• Pedestrian Classification. We aim to detect humans in images by a pattern classification approach. Rather than trying to locate various body parts (e.g. head, hands, feet) in images explicitly based on prior knowledge about human appearance, we describe a region of interest in terms of low-level features and aggregate the latter into a feature vector. In an off-line training phase, a pattern classifier derives an internal pedestrian representation using a large numbers of previously categorized feature vectors. In the on-line recall phase the derived representation is used to classify unknown samples. Issues to be resolved are data normalization (e.g. size, contrast), feature selection (e.g. normalized pixel intensities, wavelets), dimensionality reduction (e.g. PCA, ICA) and actual pattern classification (e.g. SVM, NN).
  
  Extensive image data, both from the visible and infrared spectrum, is available from Daimler. Existing MATLAB toolboxes for pattern classification can be used. System implementation for the recall phase is under C/C++, with emphasis on real-time considerations. The MS Thesis may lead to a publication.
   
• Deformable Contours (“Snakes”). Many of our techniques for object recognition involve classification techniques, which require large amounts of labeled data. The labeling task, in which a user for example outlines contours of certain objects in images, is not only tedious but also very time consuming. We want to develop computer vision techniques to assist the user in the above labeling task, allowing for some degree of automation. Of particular interest are so-called deformable contours which, after being placed over an image, adapt like "snakes" to better fit the image data. Objects of interest might be pedestrians, vehicles, faces, etc., but the resulting “PowerAssist” tool should be general-purpose.

Applicants should have an above-average academic record, a basic understanding of calculus and linear algebra, and possess strong programming skills (C/C++). Experience with image processing and/or pattern recognition techniques is a plus. Good English skills required, basic understanding of Dutch or German a plus.

Applicants should be available full-time for at least 5 months (longer is preferred). At Daimler financial assistance is available. Prospective applicants please email

• cover letter (explaining suitability)
• resume
• copy of academic record (courses/grades)
• any supporting materials (project reports etc.)

to Prof. Dr. D.M. Gavrila.