Local patch tracking algorithm based on stacked denoising autoencoder
DAI Bo
HOU Zhi-qiang
YU Wang-sheng
LI Ming
WANG Xin
JIN Ze-fenfen
Abstract：The expression and extraction of the feature plays the most important role in a visual tracking system. Based on the theory of deep learning, we propose a feature extractor based on multiple ensemble autoencoders which can decide the result by jointly describing the data input. Based the proposed network architecture, a novel tracking method applying deep features of various local patches is established. The process of breaking the input images into patches decreases the calculation complexity from amounts of multiplications to the combination of relatively less multiplications and some additions, thus reducing the time complexity. In the tracking process, the weights of different patches change according to the reliability of the corresponding ones, which improves the robustness of the tracker to conduct some challenging situations, such as light change, target posture change and occlusion. Experiments on an open tracking benchmark show that both the robustness and the timeliness of the proposed tracker are promising.
Keywords：target trackingfeature extractordeep learningparticle filterautoencoder
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 829-836 )