Propagating spatio-temporal state and progressively associating trajectory for satellite video multi-object tracking

High-resolution video satellites enable large-view dynamic monitoring for earth observation. Among satellite video interpretation techniques, multi-object tracking (MOT) receives growing attention for its foundational role. Rigid targets in satellite videos exhibit strong inter-frame appearance and posture consistency, showing quasi-linear trajectories with constrained displacements. Inspired by these kinematic characteristics, this paper introduces an online end-to-end P ropagating S patio-temporal S tate and P rogressively A ssociating T rajectory MOT (PS2PAT-MOT) framework. It consists of a detection branch for locating multi-category and multi-target objects in current frame, and a correlation branch using an inter-frame spatio-temporal state propagation (STSP) module to propagate location and appearance information and encode same-target correlations between adjacent frames. Detection and correlation outputs from both branches undergo affinity calculation, while the Progressively Associating Trajectory (PAT) strategy generates continuous tracklets using differentiated association thresholds for distinct trajectory segments. Experimental results on two publicly available AIR-MOT, SAT-MTB, and a self-built LV-SatMOT ( L arge- V iew Sat ellite video MOT ) datasets demonstrate the effectiveness of the proposed PS2PAT-MOT framework. Codes are available at: https://github.com/HELOBILLY/PS2PAT-MOT .