Identifying objects, tracking objects and predicting time series are among the basic challenges in machine vision. Deep learning has taken great steps in solving these challenges; But for many problems, satisfactory solutions that have useful applications in reality and can be used have not yet been found. In this issue, we are facing two challenges of tracking and identifying objects, and to solve this problem, it is proposed to find limiting tubes for the movement of objects in the space-time domain. Usually, object tracking and object detection are considered as two separate processes, which have been greatly improved through deep learning for 2D images. Object tracking by object detection requires that the object is successfully detected in the first frame and in all subsequent frames, and thus, by associating the results obtained from object detection, we performed the tracking operation by the TPN pipeline. The operation of identifying objects and tracking objects through a single network is still challenging and debatable. In this paper, a network structure is proposed that was able to identify a moving and moving object that was enclosed, using R-CNN Faster. In this network, we replaced TPN with RPN, and this led to better object identification and improved tracking. In this method, we tracked objects using object detection operations.