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This paper presents a framework based on Random forest using local feature descriptors to detect human in dynamic camera. The contribution presents two issues for dealing with the problem of human detection in variety of background. First, it presents the local feature descriptors based on multi scales based Histograms of Oriented Gradients (HOG) for improving the accuracy of the system. By using local feature descriptors based multiple scales HOG, an extensive feature space allows obtaining high-discriminated features. Second, machine detection system using cascade of Random Forest (RF) based approach is used for training and prediction. In this case, the decision forest based on the optimization of the set of parameters for binary decision based on the linear support vector machine (SVM) technique. Finally, the detection system based on cascade classification is presented to speed up the computational cost.

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Article Details

Issue: Vol 18 No 3 (2015)
Page No.: 199-207
Published: Aug 30, 2015
Section: Engineering and Technology - Research article

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Hoang, V. D., Le, M. H., Kang, H.-D., & Jo, K.-H. (2015). Local descriptors based random forests for human detection. Science and Technology Development Journal, 18(3), 199-207.

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