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Publisher: Springer-Verlag
Languages: English
Types: Other
Subjects:
This paper proposes a novel approach that detects and tracks carried objects by modelling the person-carried object relationship that is characteristic of the carry event. In order to detect a generic class of carried objects, we propose the use of geometric shape models, instead of using pre-trained object class models or solely relying on protrusions. In order to track the carried objects, we propose a novel optimization procedure that combines spatio-temporal consistency characteristic of the carry event, with conventional properties such as appearance and motion smoothness respectively. The proposed approach substantially outperforms a state-of-the-art approach on two challenging datasets PETS2006 and MINDSEYE2012.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • 8. D. Kroon and C. H. Slump. Coherence filtering to enhance the mandibular canal in cone-beam ct data. In Proceedings of the 4th Annual Symposium of the IEEEEMBS Benelux Chapter, pages 41-44, 2009.
    • 9. H. Nanda, C. Benabdelkedar, and L. S. Davis. Modelling pedestrian shapes for outlier detection: A neural net based approach. Proc. Intelligent Vehicles Symp, pages 428-433, 2003.
    • 10. H. Pirsiavash, D.Ramanan, and C. C. Fowlkes. Globally-optimal greedy algorithms for tracking a variable number of objects. In CVPR, pages 1201-1208, 2011.
    • 11. C. Stauffer and W. E. L. Grimson. Learning patterns of activity using real-time tracking. PAMI, 22:747-757, 2000.
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    • 14. Y. Yang and D. Ramanan. Articulated pose estimation using flexible mixtures of parts. CVPR, 2011.
    • 15. Qian Yu and Gerard Medioni. Multiple-target tracking by spatiotemporal monte carlo markov chain data association. PAMI, 31, 2009.
    • 16. J. Zunic and P. L. Rosin. A convexity measurement for polygons. PAMI, 26:173- 182, 2002.
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