• Title/Summary/Keyword: OS-ELM

Search Result 2, Processing Time 0.015 seconds

Vision-based Vehicle Detection Using HOG and OS Fuzzy-ELM (HOG와 OS 퍼지-ELM를 이용한 비전 기반 차량 검출 시스템)

  • Yoon, Changyong;Lee, Heejin
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.25 no.6
    • /
    • pp.621-628
    • /
    • 2015
  • This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.

Self-adaptive Online Sequential Learning Radial Basis Function Classifier Using Multi-variable Normal Distribution Function

  • Dong, Keming;Kim, Hyoung-Joong;Suresh, Sundaram
    • 한국정보통신설비학회:학술대회논문집
    • /
    • 2009.08a
    • /
    • pp.382-386
    • /
    • 2009
  • Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

  • PDF