Name
Abstract | ||
|---|---|---|
We present a novel approach for pedestrian intention recognition for advanced video-based driver assistance systems using a Latent-dynamic Conditional Random Field model. The model integrates pedestrian dynamics and situational awareness using observations from a stereo-video system for pedestrian detection and human head pose estimation. The model is able to capture both intrinsic and extrinsic class dynamics. Evaluation of our method is performed on a public available dataset addressing scenarios of lateral approaching pedestrians that might cross the road, turn into the road or stop at the curbside. During experiments, we demonstrate that the proposed approach leads to better stability and class separation compared to state-of-the-art pedestrian intention recognition approaches. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | Conditional random field,Computer vision,Pedestrian,Situation awareness,Advanced driver assistance systems,Pose,Artificial intelligence,Engineering,Pedestrian detection,Human head |
DocType | ISSN | Citations |
Conference | 1931-0587 | 4 |
PageRank | References | Authors |
0.41 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Schulz | 1 | 28 | 1.96 |
| Rainer Stiefelhagen | 2 | 3512 | 274.86 |