光学学报, 2010, 30 (5): 1291, 网络出版: 2010-05-11
基于自适应多特征整合的红外目标跟踪
Infrared Object Tracking Based on Adaptive Multi-Features Integration
红外目标跟踪 多特征整合 观测模型 粒子滤波 infrared object tracking multi-features integration observation model particle filtering
摘要
建立有效的观测模型去区分目标与背景是实现稳健跟踪的核心。提出了一种基于多特征观测的红外目标跟踪算法。灰度特征、局部标准差特征和梯度特征均以直方图的形式描述目标外观;目标观测模型的构建则根据环境自适应权衡了以上三个特征,相应的各特征权值的动态选择通过最大化目标与其他图像区域的差异完成。在粒子滤波框架下,实现目标运动状态的估计与多特征权值的选择。真实场景实验结果表明,该算法在目标外观高动态、背景强杂波的红外目标跟踪中具有更强的稳健性。
Abstract
Designing on effective observation model to discriminate object region from complex background is the core of robust tracking. A tracking approach based on multi-features observation has been proposed for infrared image sequences. Object appearance is represented by gray value,local standard deviation and gradient features in a unified histogram form;a scence-adaptive weighting scheme for these three features is used to construct the observation model,the selection of these multifeatures weights is towards the direction of maximizing discriminability between the target and its adjacent background. Experimental results on real complex situation demonstrate that the proposed algorithm tracks target well in highly appearance changes and severe clutter.
张辉, 赵保军, 唐林波, 李建科. 基于自适应多特征整合的红外目标跟踪[J]. 光学学报, 2010, 30(5): 1291. Zhang Hui, Zhao Baojun, Tang Linbo, Li Jianke. Infrared Object Tracking Based on Adaptive Multi-Features Integration[J]. Acta Optica Sinica, 2010, 30(5): 1291.