Skip to main content
placeholder image

Vehicle tracking by non-drifting mean-shift using projective Kalman filter

Conference Paper


Download full-text (Open Access)

Abstract


  • Robust vehicle tracking is essential in traffic monitoring because it is the groundwork to higher level tasks such as traffic control and event detection. This paper describes a new technique for tracking vehicles with mean-shift using a projective Kalman filter. The shortcomings of the mean-shift tracker, namely the selection of the bandwidth and the initialization of the tracker, are addressed with a fine estimation of the vehicle scale and kinematic model. Indeed, the projective Kalman filter integrates the non-linear projection of the vehicle trajectory in its observation function resulting in an accurate localization of the vehicle in the image. The proposed technique is compared to the standard Extended Kalman filter implementation on traffic video sequences. Results show that the performance of the standard technique decreases with the number of frames per second whilst the performance of the projective Kalman filter remains constant.

Publication Date


  • 2008

Citation


  • P. L. M. Bouttefroy, A. Bouzerdoum, S. Lam. Phung & A. Beghdadi, "Vehicle tracking by non-drifting mean-shift using projective Kalman filter," in International IEEE Conference on Intelligent Transportation Systems, 2008, pp. 61-66.

Scopus Eid


  • 2-s2.0-60749084384

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=10492&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3156

Has Global Citation Frequency


Start Page


  • 61

End Page


  • 66

Place Of Publication


  • Beijing, China

Abstract


  • Robust vehicle tracking is essential in traffic monitoring because it is the groundwork to higher level tasks such as traffic control and event detection. This paper describes a new technique for tracking vehicles with mean-shift using a projective Kalman filter. The shortcomings of the mean-shift tracker, namely the selection of the bandwidth and the initialization of the tracker, are addressed with a fine estimation of the vehicle scale and kinematic model. Indeed, the projective Kalman filter integrates the non-linear projection of the vehicle trajectory in its observation function resulting in an accurate localization of the vehicle in the image. The proposed technique is compared to the standard Extended Kalman filter implementation on traffic video sequences. Results show that the performance of the standard technique decreases with the number of frames per second whilst the performance of the projective Kalman filter remains constant.

Publication Date


  • 2008

Citation


  • P. L. M. Bouttefroy, A. Bouzerdoum, S. Lam. Phung & A. Beghdadi, "Vehicle tracking by non-drifting mean-shift using projective Kalman filter," in International IEEE Conference on Intelligent Transportation Systems, 2008, pp. 61-66.

Scopus Eid


  • 2-s2.0-60749084384

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=10492&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3156

Has Global Citation Frequency


Start Page


  • 61

End Page


  • 66

Place Of Publication


  • Beijing, China