Abstract
Appearing as an important task in computer vision, pedestrian detection has been widely investigated in the recent years. To design a robust detector, we propose a feature descriptor called Local Response Context (LRC). This descriptor captures discriminative information regarding the surrounding of the person’s location by sampling the response map obtained by a generic sliding window detector. A partial least squares regression model using LRC descriptors is learned and employed as a second classification stage (after the execution of the generic detector to obtain the response map). Experiments based on the ETHZ pedestrian dataset show that the proposed approach improves significantly the results achieved by the generic detector alone and is comparable to the state-of-the-art methods.
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Schwartz, W.R., Davis, L.S., Pedrini, H. (2011). Local Response Context Applied to Pedestrian Detection. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_21
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DOI: https://doi.org/10.1007/978-3-642-25085-9_21
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