摘要
在视频目标跟踪研究中,基于机器学习的理论和算法成为了一个重要的发展方向。在线学习通过对样本持续的学习和更新从而适应背景环境以及目标的变化,能够获得更好的目标跟踪效果。根据算法的特点,将在线学习方法分为集成学习方法、判别式学习方法和核函数学习方法3类。重点对每类中具有代表性的几种方法进行详细描述,并分析其优缺点。最后还分析了机器学习方法在目标跟踪研究中面临的问题和未来的研究趋势。
The theories and algorithms based on machine learning on video target tracking become an important direction of development. On-line learning, through continuous learning and update of the sample to adapt background environrnent and the change of target, performs better in target tracking. According to the characteristics of the algorithms, the on-line learning methods is divided into ensemble learning method, discriminant learning method and kernel learning method. The detailed descriptions of the representative methods for each class were presented. Finally, the challenges of applying machine learning to target tracking and some interesting research trends were pointed out.
出处
《计算机科学》
CSCD
北大核心
2016年第12期1-7,35,共8页
Computer Science
基金
中国科学院科技创新基金项目(YJ14K017)资助
关键词
目标跟踪
机器学习
算法
综述
Target tracking, Machine learning, Algorithm, Survey
