The research of traditional shadow detection is mainly based on the stationary camera. As the dual PTZ camera system can obtain both the multi-view and multi-resolution information, it has received more and more attention in real surveillance applications. However, few works about shadow detection and removal with such system were found in literature. In this paper, we propose a novel framework to automatically detect and remove shadow regions in real surveillance scenes from dual-PTZ-camera system. Our method consists of two stages. (1) In the first stage, the initial shadow regions are detected by comparing the similarities of pixel gray between two camera images after the homography transformation. We have demonstrated that the corresponding shadow points on a reference plane are related by a time-variant homography constraint as the camera parameters changing. (2) In the second stage, the detection of shadow region is treated as a superpixel classification problem, the predicted shadow candidates in the first stage are fed to a statistical model based on multi-feature fusion. We prove the effectiveness of the proposed shadow detection method by incorporating it with a dual-PTZ camera tracking system.