Although the object part segmentation is widely applied to surveillance video analysis and smart recommendation and so on, however, it does not show a good performance in cross-domain testing. This means the segmentation model has to label various data in different scenarios and it is costly due to the time and labor cost. Accordingly, in the paper, we would like to propose a real-time cross-domain object part segmentation system (COPS) based on the work of Cross-domain Human Parsing via Adversarial Feature and Label Adaptation [2]. Several vital techniques are applied in this real-time cross-domain object part segmentation system, including object detection, object tracking, and cross-domain adaptation object part segmentation. Taking an unconstrained benchmark dataset with rich pixel-wise labeling as the source domain, the real-time cross-domain object part segmentation system aims to segment frames of target domain videos without any additional manual labeling in real-time. Compared with the traditional approaches, this system is demonstrated to be a highly efficient and useful one among most practical applications, and the exploration on the challenging issue will contribute our real-time cross-domain object part segmentation system and push human parsing into next step. Therefore, we would like to present the details of our real-time cross-domain object part segmentation system in the following parts.