ProjectPaFiSS

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

ProjectPaFiSS

Deformable Object Tracking

Deformable Object Tracking

Scientific Director: Dr. Slobodan Ilic , Prof. Dr. Nassir Navab

Contact Person(s): Vasileios Belagiannis

In industrial collaboration with:
EADS

Keywords: Computer Vision

Abstract

We address the problem of visual tracking of arbitrary objects that undergo signifi cant scale and appearance changes. The classical tracking methods rely on the bounding box surrounding the target object. Regardless of the tracking approach, the use of bounding box quite often introduces background information. This information propagates in time and its accumulation quite often results in drift and tracking failure. This is particularly the case with the particle filtering approach that is often used for visual tracking. However, it always uses a bounding box around the object to compute features of the particle samples. Since this causes the drift, we propose to use segmentation for sampling. Relying on segmentation and computing the colour and gradient orientation histograms from these segmented particle samples allows the tracker to easily adapt to the object's deformations, occlusions, orientation, scale and appearance changes. We propose two particle sampling strategies based on segmentation. In the rest, segmentation is done for every propagated particle sample, while in the second only the strongest particle sample is segmented. Depending on this decision there is obviously a trade-o between speed and performance. This project comes from a collaboration with EADS Innovations Works, Germany.

Pictures

Figure 1: Results
Figure 2: Results

Team

Contact Person(s)

Working Group

Slobodan Ilic
Dr. Slobodan Ilic
Vasileios Belagiannis
Dr. Vasileios Belagiannis

Location



Technische Universität München
Institut für Informatik / I16
Boltzmannstr. 3
85748 Garching bei München

Tel.: +49 89 289-17058
Fax: +49 89 289-17059
Visit our lab at Garching.



internal project page

Please contact Vasileios Belagiannis for available student projects within this research project.

Dataset

seq01 seq02 seq03 seq04 seq05 seq06 seq07 seq08 seq09 seq10 seq11 seq12 seq13

You can download the evaluation dataset with the annotations HERE .

Evaluation Sequences No. of Frames Image Resolution
01. Actions 2 [29] 2113 960X540
02. Entrance 196 720X576
03. Exit 1 186 720X576
04. Exit 2 172 720X576
05. Skiing [7] 81 640X360
06. UAV [28] 716 368X288
07. Bridge 55 720X576
08. Pedestrian 1 [27] 379 640X480
09. Pedestrian 2 [26] 352 720X576
10. Cliff -dive 1 [7] 76 400X226
11. Mountain-bike [7] 228 640X360
12. Motocross 2 [7] 23 640X360
13. Head 231 320X240

Extra Video Results

  • Comparison with the tracker of Bibby & Reid [12].

  • More results.

Related Work

V. Belagiannis, F. Schubert, N. Navab, S. Ilic
Segmentation Based Particle Filtering for Real-Time 2D Object Tracking
12th European Conference on Computer Vision (ECCV), Firenze, Italy, October 07-13, 2012 (bib)

References

[7] Godec, M., Roth, P., Bischof, H.: Hough-based tracking of non-rigid objects. In: ICCV. (2011)

[12] Bibby, C., Reid, I.: Robust real-time visual tracking using pixel-wise posteriors. ECCV (2008)

[18] Babenko, B., Yang, M., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR. (2009)

[19] Kalal, Z., Matas, J., Mikolajczyk, K.: Pn learning: Bootstrapping binary classi ers by structural constraints. In: CVPR. (2010)

[26] Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You'll never walk alone: Modeling social behavior for multi-target tracking. In: ICCV. (2009)

[27] Leibe, B., Schindler, K., Van Gool, L.: Coupled detection and trajectory estimation for multi-object tracking. In: ICCV. (2007)

[28] Ollero, A., Lacroix, S., Merino, L., Gancet, J., Wiklund, J., Remuss, V., Perez, I., Gutierrez, L., Viegas, D., Benitez, M., et al.: Multiple eyes in the skies: architecture and perception issues in the comets unmanned air vehicles project. Robotics & Automation Magazine, IEEE (2005)

[29] Lockheed-Martin: Ucf lockheed-martin uav dataset. http://vision.eecs.ucf.edu/aerial/index.html (2009)


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