10 April 2018 Border-oriented post-processing refinement on detected vehicle bounding box for ADAS
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106150B (2018) https://doi.org/10.1117/12.2305330
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
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.
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Xinyuan Chen, Xinyuan Chen, Zhaoning Zhang, Zhaoning Zhang, Minne Li, Minne Li, Dongsheng Li, Dongsheng Li, } "Border-oriented post-processing refinement on detected vehicle bounding box for ADAS", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150B (10 April 2018); doi: 10.1117/12.2305330; https://doi.org/10.1117/12.2305330
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