Single target tracking algorithm based on multi-feature fusion

Abstract

Aiming at the problems of target scale change, color similarity and occlusion during target tracking, this paper proposes a single target tracking algorithm based on the fusion feature of color feature (CN) and direction gradient histogram (HOG). Under the relevant filtering and tracking framework, the original RGB color space is mapped to the color attribute space to reduce the target color from being affected by environmental changes during the tracking process. The adaptive component dimensionality reduction through principal component analysis (PCA) method, features The number of channels drops from 10 to 2, and the cost of crossing different feature subspaces is increased by smoothing constraints. At the same time, the direction gradient histogram is extracted, and the feature map is calculated by kernel correlation filtering to obtain the correlation response map, and the maximum response value is found from the response map to determine the target position. 36 groups of color video sequences were selected on the OTB standard data set for experiments. The popular correlation filter tracking algorithm was compared. The experimental results show that the algorithm has high recognition accuracy and can be used in complex environments such as illumination changes, target occlusion and deformation. Stable tracking target.