원문정보
초록
영어
In order to solve the problem of pedestrian detection performance, the described operator was improved. In this paper, semantic local binary pattern (SLBP) and histogram of oriented gradient (HOG) are combined as new feature operator. This feature method would enrich the information and enhance the detection performance. And then histogram intersection kernel support vector machine (HIKSVM) classifier is trained by the augment feature. Because the time cost is too large by the conventional SVM. HIKSVM could make up this drawback, and significantly reduce the training time. The experiments on the INRIA pedestrian dataset show that the method obtained significant improvement in accuracy comparing to HOG descriptors.
목차
Abstract
1. Introduction
2. The Overview of the Proposed Human Detection Algorithm
3. Detailed Description of HOG Feature, SLBP Feature and HIKSVM Classifier
3.1 HOG Feature
3.2. SLBP Feature
3.3 The Description of HIKSVM
4. Experimental Results and Analysis
5. Conclusio
References
1. Introduction
2. The Overview of the Proposed Human Detection Algorithm
3. Detailed Description of HOG Feature, SLBP Feature and HIKSVM Classifier
3.1 HOG Feature
3.2. SLBP Feature
3.3 The Description of HIKSVM
4. Experimental Results and Analysis
5. Conclusio
References
저자정보
참고문헌
자료제공 : 네이버학술정보