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Border-oriented post-processing refinement on detected vehicle bounding box for ADAS
Author(s): Xinyuan Chen; Zhaoning Zhang; Minne Li; Dongsheng Li
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Paper Abstract

We investigate a new approach for improving localization accuracy of detected vehicles for object detection in advanced driver assistance systems(ADAS). Specifically, we implement a bounding box refinement as a post-processing of the state-of-the-art object detectors (Faster R-CNN, YOLOv2, etc.). The bounding box refinement is achieved by individually adjusting each border of the detected bounding box to its target location using a regression method. We use HOG features which perform well on the edge detection of vehicles to train the regressor and the regressor is independent of the CNN-based object detectors. Experiment results on the KITTI 2012 benchmark show that we can achieve up to 6% improvements over YOLOv2 and Faster R-CNN object detectors on the IoU threshold of 0.8. Also, the proposed refinement framework is computationally light, allowing for processing one bounding box within a few milliseconds on CPU. Further, this refinement method can be added to any object detectors, especially those with high speed but less accuracy.

Paper Details

Date Published: 10 April 2018
PDF: 9 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150B (10 April 2018); doi: 10.1117/12.2305330
Show Author Affiliations
Xinyuan Chen, National Univ. of Defense Technology (China)
Zhaoning Zhang, National Univ. of Defense Technology (China)
Minne Li, National Univ. of Defense Technology (China)
Dongsheng Li, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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