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- W2133711945 abstract "In this paper, an integrated solution of moving object categorization is proposed, within the context of the visual surveillance system. Tracked targets are classified into four categories: single pedestrian, car, bicycle, and others (also the negative samples). In our framework, a set of strong classifier ensembles is in the form of multi-level particular features, where each feature represents an observable or derivable description associated with a tracked object. Here the classifiers based on heterogeneous features can be divided into two categories: 1) low-level numeric features (with low dimension), being classifier via AdaBoost algorithm, such as blob aspect ratio, blob area, and optical flow based velocity; 2) middle-level vector-descriptor features (with high dimension), being classifier via SVM approach, such as shape context, active template, and histogram of gradient. These classifier ensembles are then organized into a decision tree structure which is based on a proposed feature ranking technique. Compared with the similar AdaBoost and JointBoost framework quantitatively, our approach shows better results using a public LHI benchmark." @default.
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- W2133711945 date "2009-10-01" @default.
- W2133711945 modified "2023-10-14" @default.
- W2133711945 title "A Tree-Structure Classifier Ensemble for Tracked Target Categorization" @default.
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- W2133711945 doi "https://doi.org/10.1109/cisp.2009.5301454" @default.
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