Name
Abstract | ||
|---|---|---|
Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature become one of the most popular techniques used for vehicle detection in recent years. And the computing time of SVM is a main obstacle to get real time implementation which is important for Advanced Driver Assistance Systems (ADAS) applications. One of the effective ways to reduce the computing complexity of SVM is to reduce the dimension of HOG feature. In this paper, we examine the effect of the number of HOG bins on the vehicle detection and the symmetric characteristics of HOG feature of vehicle. And we successfully demonstrate the speed-up of SVM classifier for vehicle detection by about three times while maintaining the detection performance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISCE.2015.7177766 | 2015 International Symposium on Consumer Electronics (ISCE) |
Keywords | Field | DocType |
ADAS,vehicle detection,HOG,SVM | Obstacle,Computer vision,Histogram,Pattern recognition,Computer science,Advanced driver assistance systems,Support vector machine,Vehicle detection,Feature extraction,Histogram of oriented gradients,Artificial intelligence,Classifier (linguistics) | Conference |
ISSN | Citations | PageRank |
0747-668X | 4 | 0.45 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| seunghyun lee | 1 | 4 | 0.45 |
| Min-Suk Bang | 2 | 5 | 1.89 |
| Kyeong-hoon Jung | 3 | 18 | 7.03 |
| Kang Yi | 4 | 46 | 6.66 |