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Unification of the detection, tracking and classification for multiple targets is an object for random finite sets based filters developing. Introduction of target attribute information can improve tracking performance. Then, as the improved labeled particle probability hypothesis density (IL-P-PHD) filter is capable of joint detection and tracking, we will fuse obtained target attribute information...
As is known, the most prominent advantage of Finite Set Statistics (FISST) based multi-target tracking algorithms is it could cope with complicated tracking problems arising from special events such as target birth, target death and tracks crossing without complicated data association. Through improving the existing labeled particle Probability Hypothesis Density (L-P-PHD) filter, an improved labeled...
An efficient feature extraction method for remote sensing target recognition was proposed in this paper, which was based on contourlet and kernel Fisher discriminant (KFD). After the contourlet decomposition, the contourlet features are fused (the weight for fusion is chosen by cross validation), and then KFD was used for further feature extraction, finally k-nearest-neighbor (KNN) was used for classification...
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