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Xiantao Hu

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3 papers

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AAAI Conference 2026 Conference Paper

CADTrack: Learning Contextual Aggregation with Deformable Alignment for Robust RGBT Tracking

Hao Li
Yuhao Wang
Xiantao Hu
Wenning Hao
Pingping Zhang
Dong Wang
Huchuan Lu

RGB-Thermal (RGBT) tracking aims to exploit visible and thermal infrared modalities for robust all-weather object tracking. However, existing RGBT trackers struggle to resolve modality discrepancies, which poses great challenges for robust feature representation. This limitation hinders effective cross-modal information propagation and fusion, which significantly reduces the tracking accuracy. To address this limitation, we propose a novel Contextual Aggregation with Deformable Alignment framework called CADTrack for RGBT Tracking. To be specific, we first deploy the Mamba-based Feature Interaction (MFI) that establishes efficient feature interaction via state space models. This interaction module can operate with linear complexity, reducing computational cost and improving feature discrimination. Then, we propose the Contextual Aggregation Module (CAM) that dynamically activates backbone layers through sparse gating based on the Mixture-of-Experts (MoE). This module can encode complementary contextual information from cross-layer features. Finally, we propose the Deformable Alignment Module (DAM) to integrate deformable sampling and temporal propagation, mitigating spatial misalignment and localization drift. With the above components, our CADTrack achieves robust and accurate tracking in complex scenarios. Extensive experiments on five RGBT tracking benchmarks verify the effectiveness of our proposed method.

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AAAI Conference 2026 Conference Paper

Motion-Aware Object Tracking via Motion and Geometry-Aware Cues

Hongtao Yang
Bineng Zhong
Qihua Liang
Xiantao Hu
Yufei Tan
Haiying Xia
Shuxiang Song

Understanding motion is essential for visual object tracking, especially in complex and dynamic scenarios. Yet, many existing methods rely on simplistic strategies such as template updates or temporal feature propagation, often overlooking the deeper modeling of motion information. To mitigate this limitation, we introduce a motion-aware spatio-temporal framework that enhances motion perception by explicitly matching motion patterns and modeling inter-frame motion relationships. Central to our design is a motion pattern dictionary, which encodes a diverse set of representative motion cues as learnable features. During tracking, features from the search region interact with the dictionary to retrieve the most relevant motion patterns, allowing the model to adapt to the current motion state. A dedicated decoder further incorporates temporal correlations to refine motion awareness. To complement motion modeling, we embed geometric cues into the search region features, which strengthens spatial perception, reduces ambiguity under occlusion, and improves foreground-background separation. Extensive evaluations on seven challenging benchmarks demonstrate the effectiveness of our design. In particular, MoDTrack_384 surpasses recent SOTA trackers on LaSOT by 1.2% in AUC, highlighting the benefits of motion pattern modeling and geometry-guided enhancement in mitigating tracking drift.

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AAAI Conference 2025 Conference Paper

Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking

Xiantao Hu
Ying Tai
Xu Zhao
Chen Zhao
Zhenyu Zhang
Jun Li
Bineng Zhong
Jian Yang

Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusion and enhancement of spatial features or merely leverage the sparse temporal relationships between video frames. These approaches do not fully exploit the temporal correlations in multimodal videos, making it difficult to capture the dynamic changes and motion information of targets in complex scenarios. To alleviate this problem, we propose a unified multimodal spatial-temporal tracking approach named STTrack. In contrast to previous paradigms that solely relied on updating reference information, we introduced a temporal state generator (TSG) that continuously generates a sequence of tokens containing multimodal temporal information. These temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the target. Furthermore, at the spatial level, we introduced the mamba fusion and background suppression interactive (BSI) modules. These modules establish a dual-stage mechanism for coordinating information interaction and fusion between modalities. Extensive comparisons on five benchmark datasets illustrate that STTrack achieves state-of-the-art performance across various multimodal tracking scenarios.

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