基于非均匀采样的多模型红外三维目标跟踪

针对红外三维目标跟踪过程中目标姿态变化导致跟踪器失效的问题,提出了一种基于非均匀采样的多模型方法.首先用若干个原型视图表征三维目标,将这些原型视图对应的原型形状作为目标的多模型形状表示,并建立了这些原型形状之间的转移概率矩阵.在粒子滤波框架下,以对数极坐标变换下的原型视图中目标的灰度分布特征作为参考目标模型.通过对形状转移概率采样,实现了样本形状的转移与传播.此方法提高了跟踪器对于姿态变化的鲁棒性,同时具有非均匀采样特性的对数极坐标变换可以抑制图像尺度、旋转造成的畸变,并起到压缩周边的计算量的作用.仿真结果表明,这种算法对三维目标有较好的跟踪效果.

MULTIMODAL IR 3-D TARGET TRACKING BASED ON NON-UNIFORM SAMPLING

Aiming at the tracker failure problem induced by target pose changes during IR 3-D target tracking, a multimodal method based on non-uniform sampling was presented. Firstly, several prototype views were used to represent 3-D target, the corresponding prototype shapes were regarded as the multimodal shape representation, and the transition probability matrix between these prototype shapes was established. Then, under the frame of particle filtering, the reference target models were defined as the log-polar transformed prototype views＇ gray distribution features. By sampling the shape transition probability, the transfer and propagation of the sample＇ s shape state were achieved. The method improves the robustness of the tracker to target pose＇ s change. Meanwhile, log-polar transform possessing the non-uniform sampling characteristics can suppress target＇s scale and rotate deformation, and decrease the computation burdens. Simulation results show that the proposed method can track 3-D targets effectively.