Name
Abstract | ||
|---|---|---|
Negative obstacle detection has been a challenging topic. In the previous researches, the distance that negative obstacles can be detected is so near that vehicles have to travel at a very low speed. In this paper, a negative obstacle detection algorithm from image sequences is proposed. When negative obstacles are far from the vehicle, color appearance models are used as the cues of detecting negative obstacles, while negative obstacles get closer, geometrical cues are extracted from stereo vision. Furthermore, different cues are combined in a Bayesian framework to detect obstacles in image sequences. Massive experiments show that the proposed negative obstacle detection algorithm is quite effective. The alarming distance for 0.8 m width negative obstacle is 18m, and the confirming distance is 10 m. This supplies more space for vehicles to slow down and avoid obstacles. Then, the security of the UGV running in the field can be improved remarkably. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1117/12.896288 | Proceedings of SPIE |
Keywords | Field | DocType |
Negative obstacle detection,stereo vision,image sequence,Bayesian framework,occlusion detection | Obstacle,Computer vision,Occlusion detection,Computer science,Stereopsis,Artificial intelligence,Image sequence,Bayesian probability | Conference |
Volume | Issue | ISSN |
8009 | null | 0277-786X |
Citations | PageRank | References |
2 | 0.44 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tingbo Hu | 1 | 29 | 4.05 |
| Yiming Nie | 2 | 7 | 3.33 |
| Tao Wu | 3 | 58 | 11.53 |
| Hangen He | 4 | 307 | 23.86 |