Tracing of Objects in Image Sequences Using QP_TR Trust Region Algorithm
Jia Jing · 2007
A new tracking framework based on the QP_TR trust region algorithm is proposed, in which two independent algorithms appropriate for different situations are demonstrated. In the first algorithm the constant changes of the target’s size can be precisely described. For each incoming frame, a probability distribution image of the target is created, where the target’s area turns into a blob. The scale of this blob can be determined based on local maxima of differential scale-space filters. We employ the QP_TR trust region algorithm to search the local maxima of multi-scale normalized Laplacian filter of the probability distribution image to locate the target as well as determine its scale. In the second algorithm, we combine the template matching with the QP_TR method and achieved the real time performance. In the presented tracking examples, the two algorithms demonstrate their great improvement on tracking precision and runtime performance respectively.