Abstract
For real applications of pedestrian detection, both detection speed and detection accuracy are important. In this paper we propose a detector based on effective comparison features (ECFs) for simultaneously improving detection accuracy and speed. ECFs are defined as the features helping to improve actual performance. Using only these ECFs as feature candidates for the split nodes of decision trees, our detector can achieve accurate results. As an additional benefit, detection speed is improved by earlier rejection of negative samples. Experiments are conducted using well-known benchmark datasets for pedestrian detection. The experimental results of our ECF detector show that our detection speed is 1–2 orders of magnitude faster than the speed of state-of-the-art algorithms, with comparable detection accuracy.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0016669).
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Kong, KK., Lee, JW., Hong, KS. (2016). Effective Comparison Features for Pedestrian Detection. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_34
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DOI: https://doi.org/10.1007/978-3-319-41501-7_34
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