Abstract
This paper addresses the pattern of vehicle images in the Hough space, and presents a feature to detect and classify vehicle images from samples with no vehicle contains. Instead of detecting straight line by seeking peaks in the Hough space, the Hough transform is employed in a novel way to extract features of images. The standard deviation of the columns in the Hough data is proposed as a new kind of feature to represent objects in images. The proposed feature is robust with respect to challenges, such as object dimension, translation, rotation, occlusion, distance to camera, and camera view angle. To evaluate the performance of the proposed feature, a Neural Network pattern recognition classifier is employed to classify vehicle images and non-vehicle samples. The success rate is validated via various imaging environment (lighting, distance to camera, view angle, and incompleteness) for different vehicle models.
Funding: This study was funded by THUTHUKA FUNDING INSTRUMENT (National Research Foundation Rating Track) of South Africa under Grant No. 99383.
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Tu, C., Du, S. (2018). A Hough Space Feature for Vehicle Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_14
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DOI: https://doi.org/10.1007/978-3-030-03801-4_14
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