Car tracking by quantised input LMS, QX-LMS algorithm in traffic scenes
Year
: 2006
Abstract: Abstract: The tracking algorithm is an important tool for motion analysis in computer vision. A
new car tracking algorithm is proposed which is based on a new clipping technique in the field
of adaptive filter algorithms. The uncertainty and occlusion of vehicles increase the noise in
vehicle tracking in a traffic scene, so the new clipping technique can control noise in prediction
of vehicle positions. The authors present a new quantised version of the LMS, namely the
QX-LMS algorithm, which has a better tracking capability in comparison with the clipped LMS
(CLMS) and the LMS and also involves less computation. The threshold parameter of the
QX-LMS algorithm causes controllability and the increase of tracking and convergence properties,
whereas the CLMS and LMS algorithms do not have these capabilities. The QX-LMS algorithm is
used for estimation of a noisy chirp signal, for system identification and in car tracking
applications. Simulation results for noisy chirp signal detection show that this algorithm yields a
considerable error reduction in comparison to the LMS and CLMS algorithms. The proposed
algorithm, in tracking some 77 vehicles in different traffic scenes, shows a reduction of the tracking
error relative to the LMS and CLMS algorithms.
new car tracking algorithm is proposed which is based on a new clipping technique in the field
of adaptive filter algorithms. The uncertainty and occlusion of vehicles increase the noise in
vehicle tracking in a traffic scene, so the new clipping technique can control noise in prediction
of vehicle positions. The authors present a new quantised version of the LMS, namely the
QX-LMS algorithm, which has a better tracking capability in comparison with the clipped LMS
(CLMS) and the LMS and also involves less computation. The threshold parameter of the
QX-LMS algorithm causes controllability and the increase of tracking and convergence properties,
whereas the CLMS and LMS algorithms do not have these capabilities. The QX-LMS algorithm is
used for estimation of a noisy chirp signal, for system identification and in car tracking
applications. Simulation results for noisy chirp signal detection show that this algorithm yields a
considerable error reduction in comparison to the LMS and CLMS algorithms. The proposed
algorithm, in tracking some 77 vehicles in different traffic scenes, shows a reduction of the tracking
error relative to the LMS and CLMS algorithms.
Keyword(s): tracking
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Car tracking by quantised input LMS, QX-LMS algorithm in traffic scenes
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| contributor author | هادی صدوقی یزدی | en |
| contributor author | Hadi Sadoghi Yazdi | fa |
| date accessioned | 2020-06-06T13:55:12Z | |
| date available | 2020-06-06T13:55:12Z | |
| date issued | 2006 | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3374772 | |
| description abstract | Abstract: The tracking algorithm is an important tool for motion analysis in computer vision. A new car tracking algorithm is proposed which is based on a new clipping technique in the field of adaptive filter algorithms. The uncertainty and occlusion of vehicles increase the noise in vehicle tracking in a traffic scene, so the new clipping technique can control noise in prediction of vehicle positions. The authors present a new quantised version of the LMS, namely the QX-LMS algorithm, which has a better tracking capability in comparison with the clipped LMS (CLMS) and the LMS and also involves less computation. The threshold parameter of the QX-LMS algorithm causes controllability and the increase of tracking and convergence properties, whereas the CLMS and LMS algorithms do not have these capabilities. The QX-LMS algorithm is used for estimation of a noisy chirp signal, for system identification and in car tracking applications. Simulation results for noisy chirp signal detection show that this algorithm yields a considerable error reduction in comparison to the LMS and CLMS algorithms. The proposed algorithm, in tracking some 77 vehicles in different traffic scenes, shows a reduction of the tracking error relative to the LMS and CLMS algorithms. | en |
| language | English | |
| title | Car tracking by quantised input LMS, QX-LMS algorithm in traffic scenes | en |
| type | Journal Paper | |
| contenttype | External Fulltext | |
| subject keywords | tracking | en |
| journal title | IEE Proceedings Vision, Image & Signal Processing | fa |
| journal volume | 0 | |
| journal issue | 0 | |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1010159.html | |
| identifier articleid | 1010159 |