Pedestrian proposal generation using depth-aware scale estimation

Kihong Park; Seungryong Kim; Kwanghoon Sohn

doi:10.1109/ICIP.2017.8296641

Pedestrian proposal generation using depth-aware scale estimation

Kihong Park, Seungryong Kim, Kwanghoon Sohn

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1109/ICIP.2017.8296641
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/Territory	China
City	Beijing
Period	17/9/17 → 17/9/20

Bibliographical note

Funding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP)(No.2016-0-00197)

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2017.8296641

Cite this

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title = "Pedestrian proposal generation using depth-aware scale estimation",

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.",

author = "Kihong Park and Seungryong Kim and Kwanghoon Sohn",

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doi = "10.1109/ICIP.2017.8296641",

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Park, K, Kim, S & Sohn, K 2018, Pedestrian proposal generation using depth-aware scale estimation. in 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2017-September, IEEE Computer Society, pp. 2045-2049, 24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 17/9/17. https://doi.org/10.1109/ICIP.2017.8296641

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

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N1 - Funding Information: This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP)(No.2016-0-00197) Publisher Copyright: © 2017 IEEE.

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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.

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Pedestrian proposal generation using depth-aware scale estimation

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