A Lazy Decision Approach Based on Ternary Thresholding for Robust Target Object Detection

Title
A Lazy Decision Approach Based on Ternary Thresholding for Robust Target Object Detection
Authors
Jae-Yeong LeeWonpil Yu황중원김창환
Issue Date
2014-06
Publisher
ICRA (IEEE International Conference on Robotics and Automation)
Citation
, 3924 -3929
Abstract
One of the main problems of binary classification of overlapping distributions is that there always exist misclassification errors with any value of threshold. In this paper, we propose a novel lazy decision approach for robust object detection and tracking, where decision on an uncertain observation whose evaluation lies between low and high thresholds is postponed until a clear evidence appears. As a practical application of the proposed approach, we present a sensor fusion pedestrian detection system for safe navigation of UGVs in driving environment. We combine a laser-based detection of target candidates and vision-based evaluation within the proposed lazy decision framework. Experimental results on real test data demonstrate effectiveness of the proposed approach, showing significant improvement of precision-recall performance.
URI
http://pubs.kist.re.kr/handle/201004/47705
Appears in Collections:
KIST Publication > Conference Paper
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