Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
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.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article