CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking

Abstract:Multi-Object Tracking (MOT) is essential for persistent embodied perception in camera-equipped consumer and service robots. Panoramic cameras offer wide surrounding coverage, but equirectangular projection introduces a periodic horizontal domain in which conventional planar motion models and IoU-based association become unreliable near the 0°/360° seam. In addition, large-field-of-view scenes exhibit frequent interactions, scale variation, and occlusion, while frame-wise monocular depth estimates may fluctuate over time. To address these challenges, we propose CylindTrack, a depth-aware cylindrical tracking-by-detection framework for panoramic MOT. CylindTrack introduces Depth-Temporal Trajectory Modeling (DTM) to propagate instance depth as a temporally filtered trajectory-level state, providing more stable geometric cues for association. It further incorporates Spherical Spatio-Temporal Consistency Learning (SSTC), which combines a Temporal Mixer with Spherical Geometry-Aware Attention to improve temporal coherence and panoramic geometric alignment of depth-aware representations. Finally, the Topology-Aware Cylindrical Motion Model (TCMM) lifts horizontal motion into a continuous angular state space and performs seam-consistent prediction and association under panoramic periodicity. By jointly modeling depth dynamics and panoramic topology, CylindTrack improves identity preservation and trajectory continuity. Experiments on QuadTrack and JRDB achieve 33.67/31.12 HOTA and 40.45/34.33 IDF1 at 28.56/21.34 FPS, demonstrating the effectiveness and practical online efficiency of CylindTrack as a persistent perception module for panoramic consumer and service robots. The source code will be released at this https URL.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Image and Video Processing (eess.IV)

arXiv:2606.30097 [cs.CV]

(or arXiv:2606.30097v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.30097 Focus to learn more arXiv-issued DOI via DataCite

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