Effective Feature Selection Method Using Hog-Family Feature for Human Detection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F14%3A43885982" target="_blank" >RIV/44555601:13440/14:43885982 - isvavai.cz</a>
Result on the web
<a href="http://onlinepresent.org/proceedings/vol52_2014/23.pdf" target="_blank" >http://onlinepresent.org/proceedings/vol52_2014/23.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14257/astl.2014.52.23" target="_blank" >10.14257/astl.2014.52.23</a>

Result language
angličtina
Original language name
Effective Feature Selection Method Using Hog-Family Feature for Human Detection
Original language description
Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by fea-tures which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks andmeasure the perfor-mance of each feature. Here we will consider HOG-family feature. We pro-posed the multi-scale HOG as a NEW family member in this feature group. We also combine SVM with Principal Component Analysis (PCA) to reduce di-mension of features and enhance the evaluation speed while retaining most of discriminative feature vectors.
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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