Name
Abstract | ||
|---|---|---|
Detecting heads with full variations in camera view points, human poses, appearances, and scales is a key problem for many computer vision applications. Region convolutional neural networks (RCNN) achieved considerable success in handling the variances in poses and appearances. However, RCNN are inefficient in handling human heads of diverse scales. In this work, we focus on detecting human heads in complex scenes. Starting with traditional RCNN model, we extend it by leveraging person-scene relations and propose a dimension invariant convolutional neural network (DCNN) that coarsely predicts locations and scales of heads directly from the full image. We evaluate and compare our method with famous methods by using two benchmarks datasets. Experimental results show that our method outperforms these methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EUVIP47703.2019.8946163 | 2019 8th European Workshop on Visual Information Processing (EUVIP) |
Keywords | Field | DocType |
Head detection,region convolutional neural network,dimension invariant convolutional neural network,region proposal network | Pattern recognition,Convolutional neural network,Computer science,Invariant (mathematics),Artificial intelligence,Human head | Conference |
ISSN | ISBN | Citations |
2164-974X | 978-1-7281-4497-9 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sultan Daud Khan | 1 | 0 | 1.01 |
| Habib Ullah | 2 | 26 | 5.59 |
| Mohib Ullah | 3 | 22 | 8.82 |
| Faouzi Alaya Cheikh | 4 | 168 | 38.47 |
| Azeddine Beghdadi | 5 | 562 | 83.96 |