Cross-Modal Ranking with Soft Consistency and
Noisy Labels for Robust RGB-T Tracking
Abstract. Due to the complementary benefits of visible (RGB) and
thermal infrared (T) data, RGB-T object tracking attracts more and
more attention recently for boosting the performance under adverse illumination conditions. Existing RGB-T tracking methods usually localize
a target object with a bounding box, in which the trackers or detectors
is often affected by the inclusion of background clutter. To address this
problem, this paper presents a novel approach to suppress background
effects for RGB-T tracking. Our approach relies on a novel cross-modal
manifold ranking algorithm. First, we integrate the soft cross-modality
consistency into the ranking model which allows the sparse inconsistency to account for the different properties between these two modalities.
Second, we propose an optimal query learning method to handle label
noises of queries. In particular, we introduce an intermediate variable
to represent the optimal labels, and formulate it as a l1-optimization
based sparse learning problem. Moreover, we propose a single unified
optimization algorithm to solve the proposed model with stable and effi-
cient convergence behavior. Finally, the ranking results are incorporated
into the patch-based object features to address the background effects,
and the structured SVM is then adopted to perform RGB-T tracking.
Extensive experiments suggest that the proposed approach performs well
against the state-of-the-art methods on large-scale benchmark datasets