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Tavanai, A; Sridhar, M; Gu, F; Cohn, AG; Hogg, DC (2013)
Publisher: Springer-Verlag
Languages: English
Types: Other
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!

    • 1. C. Benabdelkader and L. S Davis. Detection of people carrying objects: A motionbased recognition approach. Proc. Intl Conf. Automatic Face and Gesture Recognition, pages 378-384, 2002.
    • 2. A. Branca, M. Leo, G. Attolico, and A. Distante. Detection of objects carried by people. Proc. Intl Conf. Image Processing, 3:317-320, 2002.
    • 3. R. Cutler and L. Davis. Robust real-time periodic motion detection, analysis, and applications. PAMI, 22(8), 2000.
    • 4. D. Damen and D. Hogg. Detecting carried objects from sequences of walking pedestrians. PAMI, 34(6):1056-1067, 2012.
    • 5. P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. PAMI, 32(9):1627- 1645, 2010.
    • 6. I. Haritaoglu, R. Cutler, D. Harwood, and L. S. Davis. Backpack: Detection of people carrying objects using silhouettes. CVPR, 1:102-107, 1999.
    • 7. D. Harwood I. Haritaoglu and L.S. Davis. W4: Real-time surveillance of people and their activities. PAMI, 22(8), 2000.
    • 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.
    • 12. D. Tao, X. Li, S. J. Maybank, and W. Xindong. Human carrying status in visual surveillance. CVPR, 2006.
    • 13. K. Tsuda, M. Minoh, and K. Ikeda. Extracting straight lines by sequential fuzzy clustering. Pattern Recognition Letters, 17(6):643-649, 1996.
    • 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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