基于L2范数最小化联合模型的目标跟踪算法

Object tracking based on the joint model using L2-norm minimization

为了解决稀疏表示的跟踪算法的计算代价比较大,且目标的表观由于多种原因会发生变化的问题,提出了一种在贝叶斯推理框架下,建立结合基于全局模板的判别式模型和基于局部描述子的生成式模型的联合模型,通过L2范数最小化进行求解的目标跟踪方法.在跟踪过程中,适时地更新判别式模型中的正负模板和生成式模型中模板的系数向量,使模板具有很强的适应性和判别性.实验结果表明,与其他典型的算法相比,该算法对于光照变化、尺度变化、遮挡、旋转等情况具有较强的鲁棒性.

The computational cost of the tracking algorithm based on the sparse representation is so much large, at the same time, the target apparence changes on account of a variety of reasons,which makes the ob- ject tracking process complicated and time consuming. A joint model is reasonably proposed by combining the global template based on the discriminant model and the generation model based on the local descriptor, prop- erly solved by the L2-norm minimization solution in a bayesian inference framework, which is proved to be ef- fective and efficient. In the process of the object tracking process, the plus template and the minus template of the discriminant model and the coefficient vector of the generative model are timely updated so as to have a strong adaptability and robust discrimination. The experimental results finally show that compared with other typical algorithms, the proposed algorithm has stronger robustness in the case of illumination, scale changes, shelter, rotation and so on.