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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Watanabe, T., Ito, S., & Yokoi, K. (2009). Co-occurrence histograms of oriented gradients for pedestrian detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5414 LNCS, pp. 37–47). https://doi.org/10.1007/978-3-540-92957-4_4
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