Abstract
Over the years a number of powerful people detectors have been proposed. While it is standard to test complete detectors on publicly available datasets, it is often unclear how the different components (e.g. features and classifiers) of the respective detectors compare. Therefore, this paper contributes a systematic comparison of the most prominent and successful people detectors. Based on this evaluation we also propose a new detector that outperforms the state-of-art on the INRIA person dataset by combining multiple features.
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Wojek, C., Schiele, B. (2008). A Performance Evaluation of Single and Multi-feature People Detection. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_9
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DOI: https://doi.org/10.1007/978-3-540-69321-5_9
Publisher Name: Springer, Berlin, Heidelberg
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