Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78366
Title: Switching hypothesized measurements: A dynamic model with applications to occlusion adaptive joint tracking
Authors: Wang, Y.
Tan, T.
Loe, K.-F. 
Issue Date: 2003
Source: Wang, Y.,Tan, T.,Loe, K.-F. (2003). Switching hypothesized measurements: A dynamic model with applications to occlusion adaptive joint tracking. IJCAI International Joint Conference on Artificial Intelligence : 1326-1331. ScholarBank@NUS Repository.
Abstract: This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tracking multiple objects jointly. For a set of hypotheses, multiple measurements are acquired at each time instant. The model switches among a set of hypothesized measurements during the propagation. Two computationally efficient filtering algorithms are derived for online joint tracking. Both the occlusion relationship and state of the objects are recursively estimated from the history of measurement data. The switching hypothesized measurements (SHM) model is generally applicable to describe various dynamic processes with multiple alternative measurement methods.
Source Title: IJCAI International Joint Conference on Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/78366
ISSN: 10450823
Appears in Collections:Staff Publications

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