Name
Abstract | ||
|---|---|---|
We have already proposed an obstacles detection method using a video taken by a vehicle-mounted monocular camera. In this method, correct obstacles detection depends on whether we can accurately detect and match feature points. In order to improve the accuracy of obstacles detection, in this paper, we make comparison among four most commonly used feature detectors; Harris, SIFT, SURF and FAST detectors. The experiments are done using our obstacles detection method. The experimental results are compared and discussed, and then we find the most suitable feature point detector for our obstacles detection method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.2991/jrnal.2014.1.3.3 | JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE |
Keywords | Field | DocType |
Feature detectors, Harris, SIFT, SURF, FAST, car vision | Scale-invariant feature transform,Computer vision,Feature detection,Computer science,Monocular camera,Artificial intelligence,Detector | Journal |
Volume | Issue | ISSN |
1 | 3 | 2352-6386 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaohua Qian | 1 | 1 | 1.03 |
| Joo Kooi Tan | 2 | 105 | 29.88 |
| Hyoungseop Kim | 3 | 293 | 36.05 |
| Seiji Ishikawa | 4 | 342 | 49.06 |
| Takashi Morie | 5 | 122 | 42.31 |
| Takashi Shinomiya | 6 | 6 | 3.38 |