Abstract: Cattle monitoring can rely mainly on manual inspections in the fence farming. Once the cattle escaped near the edge of the fence, the conventional regulatory system could not fully detect and intervene in time, resulting in the loss of cattle. In this study, a cattle area monitoring and boundary-crossing early warning system was proposed for the cattle using fence-type breeding. Instance segmentation and multi-object tracking, named STWBD (Segment Tracking with Boundary Detection), were also utilized to monitor the cattle activities in real time within the fenced area. Intelligent early warnings of the potential boundary-crossing behaviors were then provided to enhance the safety level of the fenced cattle farming. Firstly, an improved RDL-YOLO11n-seg instance segmentation was constructed into the framework. The C3K2-RetBlock module was also introduced to the original YOLO11n-seg, in order to enhance the response capability to the spatial displacement of the cattle. The C2DA structure was combined to improve the expression of the multi-scale features. The Segment-LSCD module was employed to optimize the segmentation head. The object detection and instance segmentation were facilitated to accurately capture the cattle contours and postures. As a result, the spatial perception and real-time monitoring of the cattle were enhanced within the fenced area. Secondly, the DeepOCSORT algorithm was assigned an identity ID to each cattle, particularly for the continuous tracking in different scenarios. Some challenges were effectively handled, such as the small target scale, severe occlusion, low lighting, and cattle close to the boundary. The continuity and stability of the multi-object tracking were provided for the reliable trajectory data to detect the later boundary-crossing behavior. In terms of the fence boundary design, the actual structure of the cattle shed fences was combined to construct a polygonal electronic fence. The safe activity area of the cattle was defined for the continuous analysis of the movement trajectories and position distribution of the cattle. The system was integrated with some functions, such as the trajectory visualization, real-time speed tracking, and statistics of the cattle population. The STWBD was used to monitor the boundary-crossing behavior of the cattle in real time and promptly trigger the intelligent early warnings. Experimental results showed that the precision, recall, and segmentation accuracy of the RDL-YOLO11n-seg model reached 94.9%, 87.4%, and 92.5%, respectively, which were 2.0, 3.1, and 3.1 percentage points higher than the original, and the parameters were reduced by 17.8%, with a floating-point operation count of 8.6 G. The high-order tracking accuracy (HOTA), multiple object tracking accuracy (MOTA), and identity F1-Score (IDFI) of DeepOCSORT were 81.4%, 91.6%, and 95.8%, respectively, while the precision, recall, and F1 score of the escape event detection were 96.2 %, 100.0 %, and 98.1 %, respectively. The movement trajectories of the cattle were efficiently monitored in the small-scale fenced farming environments. The boundary-crossing escape events were effectively identified to provide the reliable technical support for the intelligent early warning and supervision of the cattle. This finding can provide real-time access to the cattle population, distribution, and movement patterns. While the risk was predicted after trajectory and speed analysis. The finding can offer a scientific basis for the secure and efficient management of the confined cattle farming.