Multiscale fast correlation filtering tracking algorithm based on a feature fusion model.

Summary: In scenes high in visual complexity, the identification of a moving object can be affected by changes in scale and occlusion factors during the tracking process, resulting in reduced tracking accuracy. Accordingly, to address the problem of low accuracy, a multiscale fast correlation filtering tracking algorithm based on a feature fusion model is proposed in the present work with the aim of reducing the poor tracking effects caused by occlusion discrimination and scale changes in complex scenes. The object's grayscale (GRAY) feature, histogram of oriented gradient (HOG) feature, and color name (CN) feature are reduced to dimensions and fused to form a feature matrix. Moreover, a hierarchical principal component analysis (HPCA) algorithm is used to extract visually salient features and reconstruct the feature matrix under real‐time conditions, the correlation filtering position is trained, the number of dimensions is effectively reduced, and the feature fusion matrix is used to train the multiscale fast correlation filtering, with the result that the object's position and scale can be accurately predicted. The proposed algorithm is then compared with five popular correlation filtering tracking algorithms. Experimental results demonstrate that its average tracking speed reaches a reasonable frames/second velocity; moreover, it can also achieve promising object tracking results on the OTB benchmark data sets. The tracking accuracy is superior to that of the other five correlation filtering tracking algorithms when applied to scenes featuring object occlusion and changes in scale. The proposed algorithm also exhibits better robustness and improved performance under real‐time conditions in sophisticated scenarios, including scale variation, deformation, fast motion, occlusion, and so on. [ABSTRACT FROM AUTHOR]