Detection and Recognition of Moving Objects using the Moving Mask Method and the Hidden Markov Model

• 발행기관: 한국자료분석학회
• 저자명: Wanhyun Cho Sunworl Kim Gukdong Ahn
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.13 No.1, 53~62쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2011.02.28

영문 초록

This paper proposes a new detection and recognition method for moving objects that uses the moving mask method and the hidden Markov model. First, we apply the concept of entropy to convert the pixel value in the image domain into the amount of energy change in the entropy domain. Second, we find the coarse region of moving objects by automatically generating the moving mask image from the inter-frame difference between two transformed entropy images. Third, we use the discrete wavelet transformation technique to extract proper feature vectors from the detected mask image. Fourth, we use the hidden Markov model to accurately recognize moving objects. The results indicate that our proposed method can effectively and accurately detect and recognize moving objects in image sequences.

목차

1. Introduction

2. Object Detection Method

3. Object Recognition Method

4. Experimental Results

5. Conclusions

Reference

키워드

Detection and Recognition of Moving Objects Clausius Entropy Moving mask Method Discrete Wavelet Transformation Hidden Markov Model