Name
Abstract | ||
|---|---|---|
Human detection in camera images is an important task for many autonomous robots as well as automated driving systems. The Regionlets detector was one of the best-performing approaches for pedestrian detection on the KITTI dataset when we started this work in 2015.We analysed the Regionlets detector and its performance. This paper discusses the improvements in accuracy that were achieved by the different ideas of the Regionlets detector. It also analyses what the boosting algorithm learns and how this relates to the expectations.We found that the random generation of regionlet configurations can be replaced by a regular grid of regionlets. Doing so reduces the dimensionality of the feature space drastically but does not decrease detection performance. This translates into a decrease in memory consumption and computing time during training. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.5220/0006094100260032 | ICPRAM: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS |
Keywords | Field | DocType |
Pedestrian Detection, Regionlets, Perception | Pattern recognition,Computer science,Artificial intelligence,Pedestrian detection | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niels Ole Salscheider | 1 | 2 | 1.76 |
| Rehder, E. | 2 | 23 | 2.85 |
| Martin Lauer | 3 | 21 | 8.98 |