Abstract
The purpose of this paper is to detect pedestrians from images. This paper proposes a method for extracting feature descriptors consisting of co-occurrence histograms of oriented gradients (CoHOG). Including co-occurrence with various positional offsets, the feature descriptors can express complex shapes of objects with local and global distributions of gradient orientations. Our method is evaluated with a simple linear classifier on two famous pedestrian detection benchmark datasets: “DaimlerChrysler pedestrian classification benchmark dataset” and “INRIA person data set”. The results show that proposed method reduces miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets.
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Watanabe, T., Ito, S., Yokoi, K. (2009). Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection. In: Wada, T., Huang, F., Lin, S. (eds) Advances in Image and Video Technology. PSIVT 2009. Lecture Notes in Computer Science, vol 5414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92957-4_4
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DOI: https://doi.org/10.1007/978-3-540-92957-4_4
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