Abstract
Pedestrian detection draws a mount of attention in these years. However, most of the classify-based pedestrian detection methods are facing huge training samples and high computation complexity. In this paper, it proposed a manifold learning based pedestrian detection method. First, modeling the video surveillance scene via mixed gaussian background model and collecting negative samples from the background images; Second, extract the positive and negative samples histogram of oriented gradients(HOG) features, using the local preserving projection(LPP) for dimensionality reduction; Finally, detecting the pedestrian from the input image under the framework of AdaBoost. Experiments show that the algorithm achieved good results both in speed and accuracy of pedestrian detection.
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© 2014 Springer International Publishing Switzerland
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Huang, K., Wang, F., Xu, X., Cheng, Y. (2014). Pedestrian Detection Using HOG Dimension Reducing in Video Surveillance. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume II. Advances in Intelligent Systems and Computing, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-319-07773-4_22
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DOI: https://doi.org/10.1007/978-3-319-07773-4_22
Publisher Name: Springer, Cham
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