Name
Abstract | ||
|---|---|---|
In this paper, we propose an integrated system for scale-variance pedestrian detection. It consists of two cascaded components: a multi-layer detection neural network (MLDNN) for scale-variance pedestrian detection, and a fast decision forest (FDF) for boosting detection performance with only a slight decrease in speed. Experimental results on the Caltech Pedestrian dataset show that our approach achieves state-of-the-art performance with a competitive speed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/GCCE.2017.8229275 | 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE) |
Keywords | Field | DocType |
neural network,pedestrian detection,decision trees | Pedestrian,Pattern recognition,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Boosting (machine learning),Artificial neural network,Random forest,Pedestrian detection,Detector | Conference |
Volume | ISSN | ISBN |
2017-January | 2378-8143 | 978-1-5090-4046-9 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zongqing Lu | 1 | 209 | 26.18 |
| Rixi Li | 2 | 0 | 0.34 |
| QM | 3 | 464 | 72.05 |