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The posterior probability depends on the prior state and the current likelihood, which is usually deﬁned in terms of the distance of the contour from observed edges  deﬁnes the state space by the dynamics of the control points. The dynamics of the control points are modeled in terms of a spring model, which moves the control points based on the spring stiﬀness parameters. The new state (spring parameters) of the contour is predicted using Kalman ﬁlter. The correction step uses the image observations which are deﬁned in terms of the image gradients.
Overall, additional sources of information, in particular prior and contextual information, should be exploited whenever possible to attune the tracker to the particular scenario. A principled approach to integrate these disparate sources of information will result in a general tracker that can be employed with success in business intelligence applications. Object Detection and Tracking 37 References 1. : The representation and recognition of action using temporal templates. In: Proc. IEEE Conf.
In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, New York, NY (2006) 66. : Object tracking in low-frame-rate video. In: Proc. of PIE/EI - Image and Video Communication and Processing, San Jose, CA (2005) 67. : Learning on Lie groups for invariant detection and tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Anchorage, AK (2008) 68. : Regressed importance sampling on manifolds for eﬃcient object tracking. In: 6th IEEE Advanced Video and Signal based Surveillance Conference (2009) 69.