Abstract
Pedestrian detection is an important application in computer vision. Currently, most pedestrian detection methods focus on learning one or multiple fixed models. These algorithms rely heavily on training data and do not perform well in handling various pedestrian deformations. To address this problem, we analyze the cause of pedestrian deformation and propose a method to adaptively describe the state of pedestrians’ parts. This is valuable to resolve the pedestrian deformation problem. Experimental results on the INRIA human dataset and our pedestrian pose database demonstrate the effectiveness of our method.
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Huang, Y., Huang, K., Tan, T. (2011). A Heuristic Deformable Pedestrian Detection Method. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_42
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DOI: https://doi.org/10.1007/978-3-642-19309-5_42
Publisher Name: Springer, Berlin, Heidelberg
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