结合在线学习的尺度自适应多目标跟踪算法

Adaptive-scale Multi-target Tracking Algorithm Combined with Online Learning

针对多目标跟踪技术中常出现的目标尺度变化、形变和遮挡问题,提出了一种具有良好鲁棒性的新算法。本算法采用改进的SVM分类器进行在线学习,将跟踪问题看作是一种最大间隔的结构化学习问题并对其更新方式进行改进,来寻找最优权重。同时对在线更新分类器的机制进行了调整,一定程度上减少了误差积累,使在线学习能够更好地发挥其长处。此外,算法采用相关滤波器自适应调整跟踪框大小,并提出遮挡处理和数据关联机制,使被遮挡后重新出现的多个目标编号不发生交换。经过实验验证,本算法提高了跟踪精度,在复杂的背景环境中能够较好地完成多目标跟踪任务。

To solve such problems as target scale change,deformation and occlusion in multi-target tracking,this paper proposes a new algorithm with good robustness.This algorithm adopts the improved SVM classifier for online learning,and the tracking problem is considered to be a structural learning problem with the maximum interval and the updating mode is improved to find the optimal weight.At the same time,the mechanism for the online updating of the classifier is adjusted,and the accumulated error is reduced to a certain extent,in which way online learning gives play to its advantages more adequately.In addition,the algorithm adopts the correlation filter to adaptively adjust the size of the tracking box,and proposes the mechanisms of occlusion processing and data association,so that the numbers of the targets that reappear after the occlusion are not exchanged.The experimental results prove that the proposed algorithm improves the tracking accuracy and can fulfill multi-target tracking tasks in complex background environments.