Device-free Wi-Fi tracking has become essential for ubiquitous wireless sensing. However, current device-free Wi-Fi tracking systems suffer from two limitations: First, abnormal signals interfere with tracking performance when the user walks across the direct link of the transceivers and second, the tracking error based on the velocity integral accumulates over time. This article proposes CrossTrack, the first device-free cross-link tracking system with commodity Wi-Fi. Our inspiration is to regard the cross-link behavior as an opportunity to correct the trajectory instead of disturbing noise like previous work. Our approach involves three main steps. First, we devise a metric that is capable of detecting cross-link behavior. Second, we propose a new theoretical model that identifies the cross-link position as a landmark. Third, we develop a path revision technique that utilizes this landmark to optimize the trajectory. The technique innovation of this article is to reveal the theoretical approach to transform cross-link interference into optimization in device-free tracking for the first time. We implement CrossTrack based on commercial Wi-Fi devices and conduct comprehensive experiments. Our results show that CrossTrack can reduce tracking errors by 48.75%, and the median tracking error is 0.41 m.