SOPERM-Track: Spatial Object Permanence Modeling via Height Stability and Ground Perspective

Multi-object tracking is a crucial task in intelligent surveillance, yet it remains challenging in crowded environments due to severe occlusion, perspective distortion, and appearance similarity. To address these challenges, this work proposes Spatial Object Permanence Track (SOPERM-Track), which aims to endow a motion-based tracker with human-like spatial object permanence by reconstructing spatiotemporal consistency through physical geometric constraints. Specifically, short-term height stability maintains scale continuity when object observations become unreliable due to occlusion, ground contact distance models ground-plane spatial relationships from the bottom edges of detection boxes, and a global perspective map learns scene-level perspective patterns online for global scale calibration. These complementary spatial cues form an appearance-free association mechanism to improve identity consistency under occlusion, interaction, and non-linear motion. On the MOT17, MOT20, and DanceTrack benchmarks and on the shuttlecock trajectory set, SOPERM-Track demonstrates competitive tracking performance and runtime efficiency in scenarios involving long-term occlusion, similar appearances, complex motion patterns, and fast tiny-object trajectory tracking.