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• W2103290417 abstract "It is well recognised that data association is critically important for object tracking. However, in the presence of successive misdetections, a large number of false candidates and an unknown number of abrupt model switchings that happen unpredictably, the data association problem can be very difficult. We tackle these difficulties by using a layered data association scheme. At the object level, trajectories are grown from sets of object candidates that have high probabilities of containing only true positives; by this means the otherwise combinatorial complexity is significantly reduced. Dijkstra’s shortest path algorithm is then used to perform data association at the trajectory level. The algorithm is applied to low-quality tennis video sequences to track a tennis ball. Experiments show that the algorithm is robust to abrupt model switchings, and performs well in heavily cluttered environments." @default.
• W2103290417 created "2016-06-24" @default.
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• W2103290417 date "2006-07-10" @default.
• W2103290417 modified "2023-10-17" @default.
• W2103290417 title "A Novel Data Association Algorithm for Object Tracking in Clutter with Application to Tennis Video Analysis" @default.
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• W2103290417 doi "https://doi.org/10.1109/cvpr.2006.36" @default.
• W2103290417 hasPublicationYear "2006" @default.
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