CAJ | 학술논문

提出了一种对存有安全隐患车辆的跟踪学习方法，对传统的在线学习方法进行改进，通过改善学习性能，使算法在不稳定的安全隐患车辆跟踪中发挥作用，实验结果表明，在同等条件下，该算法能够获取更多有效样本，使得分类器更快收敛。
제출료일충대존유안전은환차량적근종학습방법，대전통적재선학습방법진행개진，통과개선학습성능，사산법재불은정적안전은환차량근종중발휘작용，실험결과표명，재동등조건하，해산법능구획취경다유효양본，사득분류기경쾌수렴。
This thesis proposes a tracking method on danger-hiding vehicles, which betters traditional online learning method. Through promoting learning efficiency, this method puts into work in the unstable tracking of danger-hiding vehicles. The experiment data tells that this method, under the same conditions, can get more effective samples and make classifiers having much faster convergence.