Image object tracking on integrating lie group theory with characteristic subspace eigenbasis
融合李群理论与特征子空间基的图像目标跟踪

To reduce the distortion and deformation of the object window in tracking objects with noises under complicated circumstance, we map the system state-variables to Lie group space for processing based on the affine-group invariability under disturbances. The incremental principal-component-analysis (IPCA) algorithm is employed for instant learning and updating characteristic subspace databases of the object. In sampling particles by using the particle filters, we introduce the measurement vector to improve the precision in weight-computation. In the testing of four standard video databases Car11, no deformation of tracker window caused by noises is found, and the successful tracking ratio reaches 96 percent. These results overtake those of the tracker IVT. When compared with tracker Kwon, the algorithm complexity is significantly lower and the average execution time is effectively kept within 0.32 s/frame.