Learning background-aware and spatial-temporal regularized correlation filters for visual tracking

Jianming Zhang

doi:10.1007/s10489-022-03868-8

2022-07-26

PRE

张

张见明 (Jianming Zhang) *

DOI：10.1007/s10489-022-03868-8

原文链接

摘要

En 中文

In visual tracking, correlation Filters (CFs) have attracted increasing research attention and achieved superior performance. However, owing to the larger search area, more background information is introduced to the shifted samples, meaning that tracking errors are prone to appear in the detection stage. Accordingly, in this work, firstly, hand-crafted features and deep features extracted from pre-trained convolutional networks are combined to improve the representation ability of object appearance. For deep features, we use two different VGG networks for extraction. Secondly, in an attempt to solve the problem of the object background of the traditional CF model not being modeled over time, and owing to the lack of spatial-temporal information of the image, we propose a new background-aware and spatial-temporal regularized correlation filters model (BSTCF) that introduces the background constraint and spatial-temporal regularization. The proposed BSTCF can effectively model not only the background but also variations in the background over time. Finally, we transform the objective function of BSTCF into an unconstrained Augmented Lagrange multiplier formular to promote convergence to the global optimum solution. Moreover, we adopt the alternating direction multiplier method (ADMM) to produce three sub-problems with closed-form solution, then propose a corresponding algorithm. Based on the above, we construct an intelligent tracking system and carry out extensive experiments to test its performance on OTB-2013, OTB-2015, TC128, UAV123, and VOT2016 public datasets. The experimental results demonstrate that the tracking algorithm achieves superior performance.

Keyword:

Convolutional neural network

Correlation filter

Background-aware

Spatial-temporal

ADMM