Lee, Jae-Yeong Yu, Wonpil Hwang, Jungwon Kim, ChangHwan 2024-01-19T12:08:25Z 2024-01-19T12:08:25Z 2022-03-07 2014 1050-4729 https://pubs.kist.re.kr/handle/201004/115374 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. English IEEE A Lazy Decision Approach Based on Ternary Thresholding for Robust Target Object Detection Conference 1 IEEE International Conference on Robotics and Automation (ICRA), pp.3924 - 3929 IEEE International Conference on Robotics and Automation (ICRA) 3924 3929 US Hong Kong, PEOPLES R CHINA 2014-05-31 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) 000377221103140 2-s2.0-84929224943 Proceedings Paper