LAUSR

Recently, object tracking model based on correlation filter has achieved great success. However, most of these methods relying on correlation filter train many classifiers individually, and fail to cope with significant appearance and scale change of object and complex challenging environment. How to accurately and robustly compute object scale relative to template in visual object tracking application is a challenge topic at home and abroad. Many existing tracking algorithms are not very good at big scale variation in complex video. In order to solve the problem, a novel and effective scale computation algorithm based on tracking in detection is proposed in paper. The tracking object is divided into four parts, and compute their scale factors, then get the maximum response location by adopting kernelized correlation filter based on color attribution. Specially, aimed at the difficulty of object location, weighted coefficients are used to remove the abnormal matching points and the desired training output from common classifier is transformed into other forms. Simulation experiment result shows our proposed algorithm outperform others algorithms in color video with big scale variation.