Sönke Greve

MSc Diploma Project


The DMFCam – a dynamic mobile follow-cam


Sönke Greve


Dr. Lutz Goldmann


Goal of this study is the development of a supportive helmet-cam system for winter-sports environments. In a scenario of two persons riding down the slopes – one acting as camera man, the other as athlete – the system should be able to autonomously keep track of the athlete.

The project therefore consists of three main tasks. First is the buildup of a hardware system that enables auto-centring of the target person within the image area. This will be done by implementing a low-cost pan-tilt-unit and a webcam on top of it to be mounted on a sports-helmet. Second is the people-tracking algorithm. As speed is more important than precision in this scenario two algorithms will be tested on performance. One is “Color Blob Flow”[1] from Daimler Benz’ traffic systems based on color blobs, the other is “FragTrack”[2] based on the integral histogram data structure[3]. Third the results of “Color Blob Flow” and “FragTrack” are taken into account to implement a decision and motion-estimation algorithm. This will be used to predict the target’s position in the upcoming frame to counter-act latencies of the pan-tilt-unit and the camera system.

The implementation will be done using C++ in a linux environment with Qt, OpenCV, OpenGL and sources from the robotic framework “Robbie 13” of the Active Vision Group, University of Koblenz-Landau.


  1. Heisele, B. & Ritter, W.: Obstacle Detection Based on Color Blob Flow, Proceedings of the Intelligent Vehicles Symposium, 1995
  2. Adam, A. & Rivlin, E. & Shimshoni, I.: Robust Fragments-based Tracking using the Integral Histogram, Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition – Volume 1, Pages 798-805
  3. Porkili, F.: Integral histogram: A fast way to extract histograms in cartesian spaces. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2005.