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|Title:||Probabilistic learning and modelling of object dynamics for tracking||Authors:||Tay, T.
|Issue Date:||2001||Citation:||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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