Kernel-based spatial-color modeling for fast moving object tracking
Data(s) |
2007
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Resumo |
Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/26281/1/getPDFi.pdf Babu, R Venkatesh and Makur, Anamitra (2007) Kernel-based spatial-color modeling for fast moving object tracking. In: 32nd IEEE International Conference on Acoustics, Speech and Signal Processing, APR 15-20, 2007, Honolulu, HI. |
Publicador |
IEEE |
Relação |
http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=4217226&queryText%3D%28kernel-based+spatial-color+modeling+for+fast+moving+object+tracking%29%26openedRefinements%3D*&tag=1 http://eprints.iisc.ernet.in/26281/ |
Palavras-Chave | #Electrical Engineering |
Tipo |
Conference Paper PeerReviewed |