Name
Abstract | ||
|---|---|---|
In this paper a solution to detect wrong way drivers on highways is presented. The proposed solution is based on three main stages: Learning, Detection and Validation. Firstly, the orientation pattern of vehicles motion flow is learned and modelled by a mixture of gaussians. The second stage (Detection and Temporal Validation) applies the learned orientation model in order to detect objects moving in the lane's opposite direction. The third and final stage uses an Appearance-based approach to ensure the detection of a vehicle before triggering an alarm. This methodology has proven to be quite robust in terms of different weather conditions, illumination and image quality. Some experiments carried out with several movies from traffic surveillance cameras on highways show the robustness of the proposed solution. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74260-9_99 | ICIAR |
Keywords | Field | DocType |
opposite direction,temporal validation,different weather condition,image quality,appearance-based approach,proposed solution,driver detection,orientation pattern,optical flow,orientation model,main stage,final stage,mixture of gaussians | Computer vision,ALARM,Computer science,Flow (psychology),Image quality,Robustness (computer science),Surveillance camera,Wrong direction,Artificial intelligence,Optical flow,Mixture model | Conference |
Volume | ISSN | ISBN |
4633 | 0302-9743 | 3-540-74258-1 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gonçalo Monteiro | 1 | 49 | 3.41 |
| Miguel Ribeiro | 2 | 21 | 4.56 |
| João Marcos | 3 | 117 | 15.03 |
| Jorge Batista | 4 | 589 | 32.74 |