Abstract
The Support Vector Machine (SVM) provides a
robust, accurate and effective technique for pattern recognition
and classification. Although the SVM is essentially a binary
classifier, it can be adopted to handle multi-class classification
tasks. The conventional way to extent the SVM to multi-class
scenarios is to decompose an m-class problem into a series of two-
class problems, for which either the one-vs-one (OVO) or one-vs-
all (OVA) approaches are used. In this paper, a practical and
systematic approach using a kernelised SVM is proposed and
developed such that it can be implemented in embedded
hardware within a road-side camera. The foreground
segmentation of the vehicle is obtained using a Gaussian mixture
model background subtraction algorithm. The feature vector
describing the foreground (vehicle) silhouette encodes size, aspect
ratio, width, solidity in order to classify vehicle type (car, van,
HGV), In addition 3D colour histograms are used to generate a
feature vector encoding vehicle color. The good recognition rates
achieved in the our experiments indicate that our approach is
well suited for pragmatic embedded vehicle classification
applications.
robust, accurate and effective technique for pattern recognition
and classification. Although the SVM is essentially a binary
classifier, it can be adopted to handle multi-class classification
tasks. The conventional way to extent the SVM to multi-class
scenarios is to decompose an m-class problem into a series of two-
class problems, for which either the one-vs-one (OVO) or one-vs-
all (OVA) approaches are used. In this paper, a practical and
systematic approach using a kernelised SVM is proposed and
developed such that it can be implemented in embedded
hardware within a road-side camera. The foreground
segmentation of the vehicle is obtained using a Gaussian mixture
model background subtraction algorithm. The feature vector
describing the foreground (vehicle) silhouette encodes size, aspect
ratio, width, solidity in order to classify vehicle type (car, van,
HGV), In addition 3D colour histograms are used to generate a
feature vector encoding vehicle color. The good recognition rates
achieved in the our experiments indicate that our approach is
well suited for pragmatic embedded vehicle classification
applications.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on |
| Publisher | IEEE |
| Pages | 214-218 |
| Number of pages | 5 |
| Volume | 4 |
| ISBN (Electronic) | 978-1-4244-4738-1 |
| ISBN (Print) | 978-1-4244-4754-1 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | Proc. IEEE Int. Conf. on Intelligent Computing and Intelligent Systems, pages 214 - 218, Shanghai China, November 2009. - Shganghai, China Duration: 7 Nov 2009 → 11 Nov 2009 |
Conference
| Conference | Proc. IEEE Int. Conf. on Intelligent Computing and Intelligent Systems, pages 214 - 218, Shanghai China, November 2009. |
|---|---|
| Country/Territory | China |
| City | Shganghai |
| Period | 7/11/09 → 11/11/09 |