Abstract
In many car detection method, the candidate regions which have variation in size and aspect ratio are mostly resized into a fixed size in order to extract the same length dimensional feature. However, this process reduces local object information due to interpolation problem. Thus, this paper addresses a solution to solve a such problem by using Scalable Histogram of Oriented Gradient (SHOG). The SHOG enables to extract fixed-length dimensional features for any size of region without resizing the region to a fixed size. In addition, instead of using high dimensional features in training stage, our proposal divides the feature into several low-dimensional sub-features. Each sub-feature is trained using SVM, called weak classifier. Boosting strategy is applied for combining the weak classifier results for constructing a strong classifier. By conducting comprehensive experiments, it is found that the accuracy of SHOG is higher than standard HOG as much as 3 % and 4 %, without and with boosting, respectively.
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Wahyono, Hoang, VD., Jo, KH. (2016). Multiscale Car Detection Using Oriented Gradient Feature and Boosting Machine. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_70
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DOI: https://doi.org/10.1007/978-3-662-49381-6_70
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