Multiple 3D Object tracking for augmented reality

ABSTRACT

We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale well with the number of objects, and a proper way to deal with several objects is required. Our method combines object detection and tracking: Frame-to-frame tracking is less computationally demanding but is prone to fail, while detection is more robust but slower. We show how to combine them to take the advantages of the two approaches, and demonstrate our method on several real sequences.

References

1. H. Bay, T. Tuytelaars, and L. Van Gool. Surf: Speeded up robust features. In 9th European Conference on Computer Vision, May 2006.
2. T. Drummond and R. Cipolla. Real-time visual tracking of complex structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):932-946, July 2002.
3. G. Klein and D. Murray. Parallel tracking and mapping for small AR workspaces. In International Symposium on Mixed and Augmented Reality, Nara, Japan, November 2007.
4. V. Lepetit and P. Fua. Monocular model-based 3D tracking of rigid objects. Foundations and Trends in Computer Graphics and Vision, 1(1):1-89, 2005.
5. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
6. F. Moreno-Noguer, V. Lepetit, and P. Fua. Accurate non-iterative O(n) solution to the PnP problem. In IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, October 2007.
7. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In Conference on Computer Vision and Pattern Recognition, volume 2, pages 2161-2168, October 2006.
8. M. Ozuysal, P. Fua, and V. Lepetit. Fast keypoint recognition in ten lines of code. In Conference on Computer Vision and Pattern Recognition, Minneapolis, MI, June 2007.
9. D. Reid. An Algorithm for Tracking Multiple Targets. IEEE Trans. on Automatic Control, 24(6):843-854, Dec 1979.
10. S. Oh, S. Russell, and S. Sastry. Markov Chain Monte Carlo Data Association for General Multiple-Target Tracking Problems. In Proc. of the IEEE Conference on Decision and Control, Bahamas, Dec. 2004.
11. C. Tomasi and T. Kanade. Detection and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University, April 1991.
12. L. Vacchetti, V. Lepetit, and P. Fua. Stable real-time 3D tracking using online and offline information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(10):1391-1402, 2004.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Object recognition

        • Tracking

      • Computer vision tasks

        • Scene understanding

      • Image and video acquisition

        • 3D imaging

  • Computer graphics

    • Animation

  • Machine learning