Due to the ability to penetrate darkness, smoke, and fog, wireless sensing technologies offer unique advantages for real-world deployments. Recent studies have mounted wireless sensing devices on mobile systems (such as drones and wheeled robots). Despite their promising potential, these approaches are predominantly designed for non-human platforms that exhibit limited mobility, typically involving translational movement with minimal rotation. When deployed on human carriers, however, the frequent body rotations and complex motion patterns significantly degrade their performance, due to the unpredictable changes in target position. In this paper, we propose Mobi2Still, which utilizes mobile human-equipped millimeter wave (mmWave) radars for people detection and tracking. The core idea is to match recurring environment information across different times to estimate radar motion in reverse, and based on this, enable accurate people detection and tracking. Specifically, Mobi2Still extracts geometric structure of scene objects which is radar-motion-independent to match the recurring environmental objects, thereby adapting both rotation and translation of mmWave radars. Furthermore, to improve Mobi2Still's generalization, we design a velocity-aware calibration mechanism to guide it to focus on scene-independent motion features. Experiments demonstrate that Mobi2Still can accurately detect and track people with 98.10% F1-score and 98.32% MOTA, achieving at least 7.54% F1-score (and 11.20% MOTA) improvements compared with state-of-the-art approaches.