TAILIEUCHUNG - Local descriptors based random forests for human detection
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. | TAÏP CHÍ PHAÙT TRIEÅN KH&CN, TAÄP 18, SOÁ K6- 2015 Local descriptors based random forests for human detection Van-Dung Hoang Quang Binh University, Vietnam My-Ha Le University of Technical Education Ho Chi Minh City, Vietnam Hyun-Deok Kang Ulsan National Institute of Science and Technology, Korea Kang-Hyun Jo University of Ulsan, Korea (Manuscript Received on July 15, 2015, Manuscript Revised August 30, 2015) ABSTRACT 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. Keywords: Multi scales based HOG, Support vector machine, Random decision forest, Local descriptor. 1. INTRODUCTION In recent years, human detection systems using vision sensors have been become key task for a variety of applications, which have potential influence in modern intelligence systems knowledge integration and management in autonomous systems[1, 2]. However, there are many challenges in the detection procedures such as various articulate poses, appearances, illumination conditions and complex backgrounds of outdoor scenes, and occlusion in crowded scenes. Up to day, several successful methods for object detection .
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