RGBT Tracking via Noise-Robust Cross-Modal Ranking

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 9; pp. 5019 - 5031
• Main Authors: Li, Chenglong, Xiang, Zhiqiang, Tang, Jin, Luo, Bin, Wang, Futian
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Background noise; Clutter; Collaboration; Complementarity; Computational modeling; Consistency; Cross-modal ranking; Heterogeneity; label optimization; Lighting; Noise; Noise robustness; Occlusion; Optimization; Ranking; RGBT tracking; Robustness; soft consistency; structured support vector machine (S-SVM); Target tracking; Task analysis; Thermal sensors; Tracking
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3067107

Existing RGBT tracking methods usually localize a target object with a bounding box, in which the trackers are often affected by the inclusion of background clutter. To address this issue, this article presents a novel algorithm, called noise-robust cross-modal ranking, to suppress background effects in target bounding boxes for RGBT tracking. In particular, we handle the noise interference in cross-modal fusion and seed labels from the following two aspects. First, the soft cross-modality consistency is proposed to allow the sparse inconsistency in fusing different modalities, aiming to take both collaboration and heterogeneity of different modalities into account for more effective fusion. Second, the optimal seed learning is designed to handle label noises of ranking seeds caused by some problems, such as irregular object shape and occlusion. In addition, to deploy the complementarity and maintain the structural information of different features within each modality, we perform an individual ranking for each feature and employ a cross-feature consistency to pursue their collaboration. A unified optimization framework with an efficient convergence speed is developed to solve the proposed model. Extensive experiments demonstrate the effectiveness and efficiency of the proposed approach comparing with state-of-the-art tracking methods on GTOT and RGBT234 benchmark data sets.