摘要
在实际监控场合中,行人的运动有着诸多不确定性,这些会对现有的跟踪算法产生干扰,从而造成跟踪丢失.基于此,文中提出一种将行人检测的先验知识融入到跟踪模型自学习过程的行人跟踪算法.首先通过离线训练,得到具有较强区分能力的子分类器集,这些子分类器蕴含了对于行人的先验知识.在跟踪过程中,使用online boosting算法从离线训练的子分类器集中学习并更新强分类器,对被跟踪行人进行动态建模.实验结果表明,该算法有效缓解算法自适应性与"漂移"之间的矛盾,能够在真实监控场合下跟踪具有复杂运动的行人.
In actual surveillance conditions, many uncertainties exist in pedestrian movement. These movements may disturb the current tracking algorithms and result in tracking lost. An adaptive pedestrian tracking algorithm is proposed. In this algorithm, the prior knowledge of pedestrian detection is embedded into the self-learning process of object model. Firstly, offline training is performed to get a set of sub-classifiers with strong discriminability and prior knowledge of the pedestrians. Then, online boosting algorithm is used for learning and updating the pedestrian ＂s dynamic model from the offline trained sub classifier set. Experimental results show that the proposed method efficiently relieves the conflict between adaptation and drifting, and tracks pedestrian with various uncertain movement under the actual surveillance conditions.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第5期704-708,共5页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金广东联合基金资助项目(No.U0835002)
关键词
行人跟踪
先验知识
在线学习
Pedestrian Tracking, Prior Knowledge, Online Learning
