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

• URL: http://arxiv.org/abs/2606.30097v1
• Date: Mon, 29 Jun 2026 10:32:08 GMT
• Title: CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking
• Abstract summary: Multi-Object Tracking (MOT) is a core capability for embodied perception.<n> panoramic cameras are attractive for embodied systems because their 360 field of view reduces blind spots and keeps surrounding targets observable for longer durations.<n>CylindTrack is a depth-aware cylindrical tracking-by-detection framework for panoramic MOT.
• Score: 20.709779731310103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Object Tracking (MOT) is a core capability for embodied perception, and panoramic cameras are attractive for embodied systems because their 360° field of view reduces blind spots and keeps surrounding targets observable for longer durations. However, panoramic MOT is not a straightforward extension of perspective MOT. In equirectangular panoramic videos, the horizontal image domain is periodic rather than Euclidean, which breaks planar motion assumptions and makes IoU-based association unreliable near the 0°/360° seam. Meanwhile, large-FoV scenes often contain more objects, stronger scale variation, and more frequent interactions, making online association particularly sensitive to unstable frame-wise depth cues. To address these issues, we propose CylindTrack, a depth-aware cylindrical tracking-by-detection framework for panoramic MOT. CylindTrack first introduces Depth-Temporal Trajectory Modeling (DTM), which promotes instance depth from an isolated frame-wise cue to a temporally filtered trajectory-level state. To improve the reliability of depth observations, we further develop Spherical Spatio-Temporal Consistency Learning (SSTC), which combines a Temporal Mixer and Spherical Geometry-aware Attention to enhance temporal coherence and panoramic geometric alignment in depth-aware representations. Finally, we design a Topology-Aware Cylindrical Motion Model (TCMM) that lifts horizontal motion into a continuous angular state space and performs seam-consistent motion prediction and association in the periodic panoramic domain. By jointly modeling trajectory-level depth consistency and panoramic topology, CylindTrack improves identity preservation and trajectory continuity in challenging panoramic scenes. The source code will be released at https://github.com/warriordby/CylindTrack.

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