SUMO: 非線形状態空間モデルによる任意の動きのセグメンテーションと追跡
SUMO: Segment and Track Any Motion with Nonlinear State Space Models
視覚的手がかりに依存せず、ロボティクス由来の非線形状態空間モデルと選択的アンセンテッドフィルタを用いて、ゼロショットで動物体のセグメンテーションと追跡を統合するフレームワークを提案した。
著者: Kexin Tian, Sixu Li, Keshu Wu, Yang Zhou, Zhengzhong Tu
分類: cs.CV, cs.AI
原文アブストラクト
Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal object dynamics. Existing methods rely predominantly on visual cues and thus often falter in real-world scenarios where object motions are inherently complex and nonlinear. To address this limitation, we propose SUMO, a zero-shot, training-free, unified framework integrating nonlinear dynamics with vision-based segmentation for accurate and consistent VOT and MOS. Specifically, we develop a nonlinear State Space Model (SSM) inspired by robotics principles to capture the complex object dynamics. Building on this model, we propose a Selective Unscented Filter (SUF) for accurate state estimation, which features a joint scoring mechanism and dynamically fuses multi-source predictions to identify the most plausible object state over time. Furthermore, we apply a memory selection mechanism to evaluate the reliability of memory frames. Our extensive experimental results show that SUMO achieves state-of-the-art performance on both VOT and MOS tasks.