Abstract
The paper proposes a novel approach for classification of sports images based on the geometric information encoded in the image of a sport’s field. The proposed approach uses invariant nature of a cross-ratio under projective transformation to develop a robust classifier. For a given image, cross-ratios are computed for the points obtained from the intersection of lines detected using Hough transform. These cross-ratios are represented by a histogram which forms a feature vector for the image. An SVM classifier trained on aprior model histograms of cross-ratios for sports fields is used to decide the most likely sport’s field in the image. Experimental validation shows robust classification using the proposed approach for images of Tennis, Football, Badminton, Basketball taken from dissimilar view points.
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Paluri, B., Nalin Pradeep, S., Shah, H., Prakash, C. (2007). Sports Classification Using Cross-Ratio Histograms. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_12
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DOI: https://doi.org/10.1007/978-3-540-76390-1_12
Publisher Name: Springer, Berlin, Heidelberg
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