关键词: 视频目标跟踪, 核相关滤波, 深度学习, 深度特征

Abstract:

Aiming at the shortcomings of traditional manual features in target tracking algorithm based on kernel correlation filtering, this paper takes target tracking technology based on kernel correlation filtering as the research object, uses deep convolution neural network to automatically extract deep convolution features of target to be tracked to replace traditional manual features. The deep convolution feature extracted from different convolution layers is separately learned by the kernel correlation filter to obtain different feature maps, and the position of the target to be tracked in the video sequence is determined by weighted fusion of multiple feature maps, it improves the robustness of the tracking algorithm in complex interference background.

Key words: video target tracking, kernel correlation filtering, deep learning, deep feature