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• W2465455192 abstract "In order to detect objects efficiently and accurately, we propose a high real-time and robust performance object detection method in this paper. Firstly, a new feature called Locally Assembled Binary Haar-like (LABH) is selected as the feature vector. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar feature, then, brings in similar idea of Local Binary Patter (LBP), assemble several neighboring binary Haar feature to improve the ability of illumination invariant and discriminating power. Furthermore, an optimal parameters selection of Support Vector Machine by Particle Swarm Optimization algorithm is presented to build on an efficient classifier. The parameters of SVM have a great effect on the generalization performance of classify. In order to improve performance of SVM, the PSO is used to search the optimal parameters of the SVM model, which could improve system's performance. The experiment carried out on videos from INRIA dataset, MIT dataset and Daimler dataset illustrates that the proposed method is real-time and feasible enough for the task of object detection." @default.
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• W2465455192 date "2012-10-16" @default.
• W2465455192 modified "2023-09-24" @default.
• W2465455192 title "A New Object Detection Method Based on Locally Assembled Binary Haar Features" @default.
• W2465455192 doi "https://doi.org/10.1109/icecc.2012.21" @default.
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