Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40844
Title: Probabilistic learning and modelling of object dynamics for tracking
Authors: Tay, T.
Sung, K.K. 
Issue Date: 2001
Source: Tay, T.,Sung, K.K. (2001). Probabilistic learning and modelling of object dynamics for tracking. Proceedings of the IEEE International Conference on Computer Vision 1 : 648-653. ScholarBank@NUS Repository.
Abstract: The problem of tracking can be decomposed and independently addressed in two steps, namely the prediction step and the verification step. In this paper, we present a new approach of addressing the prediction step that is based on modelling joint probability densities of successive states of tracked objects. This approach has the advantage that it is conceptually general such that given sufficient training data, it is capable of modelling a wide range of complex dynamics. Furthermore, we show that this conceptual prediction framework can be implemented in a tractable manner using a Gaussian mixture representation which allows predictions to be generated efficiently. We then describe experiments that demonstrate these benefits.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/40844
Appears in Collections:Staff Publications

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