Grayscale-Thermal Tracking via Inverse Sparse Representation-Based Collaborative Encoding

Bin Kang, Dong Liang, Wan Ding, Huiyu Zhou, Wei-Ping Zhu

Abstract:

Grayscale-thermal tracking has attracted a great
deal of attention due to its capability of fusing two different
yet complementary target observations. Existing methods often
consider extracting the discriminative target information and
exploring the target correlation among different images as
two separate issues, ignoring their interdependence. This may
cause tracking drifts in challenging video pairs. This paper
presents a collaborative encoding model called joint correlation and discriminant analysis based inver-sparse representation
(JCDA-InvSR) to jointly encode the target candidates in the
grayscale and thermal video sequences. In particular, we develop
a multi-objective programming to integrate the feature selection
and the multi-view correlation analysis into a unified optimization
problem in JCDA-InvSR, which can simultaneously highlight the
special characters of the grayscale and thermal targets through
alternately optimizing two aspects: the target discrimination
within a given image and the target correlation across different
images. For robust grayscale-thermal tracking, we also incorporate the prior knowledge of target candidate codes into the
SVM based target classifier to overcome the overfitting caused
by limited training labels. Extensive experiments on GTOT and
RGBT234 datasets illustrate the promising performance of our
tracking framework.