arXiv AI
Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Sep 22
DOA-SORT: Directional Occlusion-Aware Multi-Object Tracking with Distributional Observations
arXiv:2609.22706v1 Announce Type: new Abstract: Identity association in multi-object tracking (MOT) is vulnerable to partial occlusion, truncated detections, and fluctuating confidence scores. Existi...
arXiv AI
Jun 17
GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture
arXiv:2602. 14771v5 Announce Type: replace-cross Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity.
arXiv Computer Vision
5d ago
OmniGlimpse: Asymmetric Identity Memory for Online Multi-View Tracking
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.
arXiv AI
Jun 24
Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking
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.
arXiv AI
Jul 21
VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects
arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.
arXiv AI
Sep 10
Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking
The paper introduces RGBTR‑Motion, a new benchmark that synchronizes RGB, thermal, and radar data with dense moving‑instance masks and consistent identities for surveillance scenes. It also presents SAM‑Radar, a segmentation and tracking framework that fuses calibrated RGBT features with radar returns, using radar‑aware detection and motion supervision to reject clutter and maintain identity continuity during low visibility or occlusion. SAM‑Radar achieves state‑of‑the‑art performance, improving IoU, F1‑50, MOTA, HOTA, and IDF1 metrics over existing methods.