Abstract
In this work, we propose an efficient method that generates pedestrian proposals suitable for the autonomous vehicle. Our main intuition is that depth information provides an important cue to assign the scale of pedestrian proposals. Based on the observation that in a 3-D world coordinate the scales of pedestrians are almost similar, we formulate the scales of pedestrian patches by projecting 3-D models to an image plane with its corresponding depth. We also introduce a scale-aware binary description using both color and depth images. By using this descriptor, the regression models are trained to rank the pedestrian proposal candidates and adjust the proposal bounding boxes for an accurate localization. Our algorithm achieves significant performance gains compared to conventional proposal generation methods on the challenging KITTI dataset.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 2045-2049 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509021758 |
| DOIs | |
| Publication status | Published - 2018 Feb 20 |
| Event | 24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 2017 Sep 17 → 2017 Sep 20 |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| Volume | 2017-September |
| ISSN (Print) | 1522-4880 |
Other
| Other | 24th IEEE International Conference on Image Processing, ICIP 2017 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 17/9/17 → 17/9/20 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
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Pedestrian proposal generation using depth-aware scale estimation. / Park, Kihong; Kim, Seungryong; Sohn, Kwanghoon.
2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2018. p. 2045-2049 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Pedestrian proposal generation using depth-aware scale estimation
AU - Park, Kihong
AU - Kim, Seungryong
AU - Sohn, Kwanghoon
PY - 2018/2/20
Y1 - 2018/2/20
N2 - In this work, we propose an efficient method that generates pedestrian proposals suitable for the autonomous vehicle. Our main intuition is that depth information provides an important cue to assign the scale of pedestrian proposals. Based on the observation that in a 3-D world coordinate the scales of pedestrians are almost similar, we formulate the scales of pedestrian patches by projecting 3-D models to an image plane with its corresponding depth. We also introduce a scale-aware binary description using both color and depth images. By using this descriptor, the regression models are trained to rank the pedestrian proposal candidates and adjust the proposal bounding boxes for an accurate localization. Our algorithm achieves significant performance gains compared to conventional proposal generation methods on the challenging KITTI dataset.
AB - In this work, we propose an efficient method that generates pedestrian proposals suitable for the autonomous vehicle. Our main intuition is that depth information provides an important cue to assign the scale of pedestrian proposals. Based on the observation that in a 3-D world coordinate the scales of pedestrians are almost similar, we formulate the scales of pedestrian patches by projecting 3-D models to an image plane with its corresponding depth. We also introduce a scale-aware binary description using both color and depth images. By using this descriptor, the regression models are trained to rank the pedestrian proposal candidates and adjust the proposal bounding boxes for an accurate localization. Our algorithm achieves significant performance gains compared to conventional proposal generation methods on the challenging KITTI dataset.
UR - http://www.scopus.com/inward/record.url?scp=85045313157&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045313157&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2017.8296641
DO - 10.1109/ICIP.2017.8296641
M3 - Conference contribution
AN - SCOPUS:85045313157
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2045
EP - 2049
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
ER -