arXiv:2606. 23604v2 Announce Type: replace-cross Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation.
RA‑SOD is a new RGB‑Thermal salient object detection framework that explicitly models the reliability of each modality. It introduces a reliability‑conditioned representation, an uncertainty‑guided dual‑stream refinement, and a pixel‑wise modality competition mechanism to adaptively compensate degraded features and suppress unreliable evidence. Experiments on four benchmarks show that RA‑SOD achieves state‑of‑the‑art performance and remains robust under severe modality degradation.
arXiv:2606. 26455v1 Announce Type: cross Abstract: RGB-Event tracking improves localization robustness by fusing RGB appearance textures and dense temporal motion cues from event sensors.
ESMTrack is a fully end‑to‑end self‑supervised RGB‑T tracking framework that eliminates the need for costly modality‑aligned bounding boxes or offline pseudo‑label generation. It learns discriminative, temporally consistent representations using a grounding triplet loss on the initial annotated frame and a cross‑frame temporal triplet loss on unlabeled search frames, with reliable samples selected via forward‑backward consistency. A three‑branch architecture (fusion, RGB, thermal) and a modality decoupling mechanism mitigate modality dominance bias, enabling competitive state‑of‑the‑art performance, strong cross‑dataset generalization, and real‑time inference on five RGB‑T benchmarks.
arXiv:2606. 01694v1 Announce Type: cross Abstract: Thermal pedestrian MOT remains challenging because weak appearance cues and frequent detection interruptions cause severe trajectory fragmentation.
arXiv:2606. 14094v1 Announce Type: cross Abstract: Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information.
UniMergeTrack introduces a hierarchical model merging strategy for RGB-X visual object tracking, combining independently trained modality-specific trackers into a unified framework. The method merges transferable backbones using high‑pass‑filtered merging, aligns tracking heads with optimal transport‑guided channel alignment, and fine‑tunes with distribution alignment to integrate cross‑task knowledge while preserving modality cues. Experiments on multiple benchmarks show state‑of‑the‑art or competitive performance across RGB‑Thermal, RGB‑Event, and RGB‑Depth tracking tasks, with strong generalization to unseen datasets.
arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.
TriDifNet is a diffusion-based network that jointly performs registration, fusion, and detection for misaligned RGB-T pedestrian perception. It includes three modules—RegDM for aligning infrared to visible images, FusDM for generating a detection-oriented fused representation, and DetDM for iterative bounding‑box denoising. The system uses closed‑loop optimization and a Nash‑MTL refinement to balance the three tasks, achieving state‑of‑the‑art detection metrics on the KAIST multispectral benchmark.
arXiv:2609.01584v1 Announce Type: new Abstract: Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traf...
arXiv:2610.34939v1 Announce Type: new Abstract: RGB-X tracking complements RGB appearance with thermal, depth, or event observations, but centralized training requires collecting multimodal videos at...
OmniGlimpse introduces an asymmetric propose‑verify framework for online multi‑view tracking, separating short‑term adaptive association from long‑term stable identity consolidation. It maps short‑term local and cross‑view evidence into target‑specific adaptive spaces for proposing associations, while maintaining long‑term identity distributions in a canonical space defined by a frozen encoder for verification. The method achieves significant gains over the best online baseline on M3Track and MvMHAT, outperforming offline‑trained references in IDF1 without any offline scene‑specific training.