基于粒子滤波的红外目标跟踪

INFRARED OBJECT TRACKING BASED ON PARTICLE FILTERS

粒子滤波(Partic le F ilter)是一种处理非线性和非高斯动态系统状态估计的有效技术.提出了一种基于粒子滤波的红外目标稳健跟踪新方法.在粒子滤波理论框架下,红外目标的状态后验概率分布用加权随机样本集表示,通过这些随机样本的Bayesian迭代进化实现红外目标的跟踪.系统状态转移模型选择为简单的二阶自回归模型,并自适应地确定系统噪声方差.红外目标的描述利用目标区域的灰度分布,该灰度分布通过核概率密度估计建立.通过计算参考目标的灰度分布和目标样本的灰度分布之间的Bhattacharyya距离,建立系统观测概率模型.实验结果表明该方法是有效的和稳健的.

The particle filter is an effective technique for the state estimation in non-linear and non-Gaussian dynamic systems. A novel method for infrared object robust tracking based on particle filters was proposed. Under the theory framework of particle filters, the posterior distribution of the infrared object is approximated by a set of weighted samples, while infrared object tracking is implemented by the Bayesian propagation of the sample set. The state transition model is chosen as the simple second-order auto-regressive model, and the system noise variance is adaptively determined in infrared object tracking. Infrared objects are represented by the intensity distribution, which is defined by the kernel-based density estimation. By calculating the Bhattacharyya distance between the object reference distribution and the object sample distribution, the observation probability model is constructed. Experimental results show that our method is effective and steady.