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- W2000500047 abstract "This paper presents a generic video vehicle detection approach through multiple background-based features and statistical learning. The main idea is to configure several virtual loops (as detection zones) on the image, assuming moving vehicles may cause pixel intensities and local texture to change, and then by identifying such pixel changes to detect vehicles. In this research, multiple pattern classifiers including LDA + Adaboost, SVM, and Random Forests are used to detect vehicles that are passing through virtual loops. We extract fourteen pattern features (related to foreground area, texture change, and luminance and contrast in the local virtual loop zone and the global image) to train pattern classifiers and then detect vehicles. As experimental results illustrate, the proposed approach is quite robust to detect vehicles under complex dynamic environments, and thus is able to improve the accuracy of traffic data collection in all weather for long term." @default.
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- W2000500047 date "2011-10-01" @default.
- W2000500047 modified "2023-10-16" @default.
- W2000500047 title "Video vehicle detection through multiple background-based features and statistical learning" @default.
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- W2000500047 doi "https://doi.org/10.1109/itsc.2011.6082948" @default.
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