## 3 Methodology
### Hardware and Software Environment  
The system is deployed on the Jetson TX2 NX edge computing platform, which features an embedded GPU with 256 CUDA cores (1.33 TFLOPS), supports up to five CSI camera inputs, and is equipped with 4 GB of LPDDR4 memory and a dedicated video codec unit. This hardware configuration makes it well-suited for real-time image correction and stitching tasks. The software environment is built on Ubuntu 18.04 and JetPack 4.5.1, ensuring driver compatibility and system stability.  
Two Hikvision B14 HV3-LT4 MM network cameras (Hikvision, Hangzhou, China), each with a resolution of 2560 \(\times\) 1440 and a frame rate of 50 Hz, are connected to the system. These cameras support TCP/IP and RTSP protocols and are controlled via the official API. A gigabit router and wired Ethernet connections are used for network communication, enabling high-throughput and concurrent video data transmission (see Figure 12).  
The graphical user interface (GUI) is developed using the Qt framework and supports key functionalities such as dual-camera image acquisition, real-time display, image registration, and stitching output. The operational interface is shown in Figure 13. To improve runtime efficiency and ensure stability on the edge platform, a multithreaded architecture is adopted to alleviate resource contention among functional modules.  
The image stitching and DeepStream-based streaming (pull/push) functionalities are configured to run on the main thread, which is prioritized for access to system computing resources. Auxiliary modules, such as image preview and button event responses, are executed in separate subthreads. These threads share key intermediate variables (e.g., GPU memory addresses) to avoid redundant computation and reduce communication overhead, thereby ensuring system stability and responsiveness during runtime.  
Figure 12: Physical setup of the system.  
Upon clicking the \"Image Calibration Preprocessing\" button, the system initiates a series of initialization tasks based on real-time image input from the cameras. These tasks include homography matrix estimation, stitching seam planning, and pixel coordinate mapping table generation. The results are cached in GPU memory (see partial illustration in Figure 14) for subsequent real-time image stitching.  
When the \"Start Stitching and Streaming\" button is triggered, the main processing pipeline begins. Dual-channel image data are fed into the GPU for stitching, and the output is streamed via a sink component to the designated network address (e.g., rtmp://192.168.1.69:93/lsw), which can be accessed in real time through clients such as VLC.  
It is worth noting that the current system is designed for automated processing, without user-adjustable parameters, to simplify the operation workflow and maintain stability across specific task scenarios. Historical data storage and access functionality are not yet integrated, but future versions will consider adding result caching and retrieval modules. During embedded deployment, the system leverages model pruning and DeepStream stream optimization strategies to significantly reduce computational redundancy and com  
Figure 14: Precomputed data stored on the edge computing platform.  
Figure 13: System user interface.  
munication latency. In practical testing, the system demonstrated smooth performance, with no noticeable UI lag or streaming delay, thus meeting the basic real-time requirements of embedded platforms.