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titleA Sparse Probabilistic Learning Algorithm for Real-Time Tracking
authorsOliver Williams, Andrew Blake, Roberto Cipolla
keywordsvisual tracking, relevance vector machine, RVM
abstractThis paper addresses the problem of applying powerful
pattern recognition algorithms based on kernels to efficient
visual tracking. Recently Avidan [1] has shown that object
recognizers using kernel-SVMs can be elegantly adapted to
localization by means of spatial perturbation of the SVM,
using optic flow. Whereas Avidan’s SVM applies to each
frame of a video independently of other frames, the benefits
of temporal fusion of data are well known. This issue is addressed
here by using a fully probabilistic ‘Relevance Vector
Machine’ (RVM) to generate observations with Gaussian
distributions that can be fused over time. To improve
performance further, rather than adapting a recognizer, we
build a localizer directly using the regression form of the
RVM. A classification SVM is used in tandem, for object
verification, and this provides the capability of automatic
initialization and recovery.
The approach is demonstrated in real-time face and vehicle
tracking systems. The ‘sparsity’ of the RVMs means
that only a fraction of CPU time is required to track at
frame rate. Tracker output is demonstrated in a camera
management task in which zoom and pan are controlled in
response to speaker/vehicle position and orientation, over
an extended period. The advantages of temporal fusion in
this system are demonstrated.
journalProceedings of ICCV 2003, 353-360
published year
(Total records:1429)
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