# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 4 Conclusions  
This paper addresses the challenges of limited field of view, jitter accumulation, and dynamic interference in online video stitching by proposing an end-to-end unsupervised deep learning framework that achieves the collaborative optimization of both stability and stitching tasks. The specific conclusions are as follows:  
1. End-to-End collaborative optimization architecture: To tackle the alignment degradation caused by phase-wise optimization, we propose an end-to-end collaborative optimization framework. By constructing a differentiable network to jointly learn stitching and stability parameters and utilizing an error backpropagation mechanism to synchronize the optimization of both tasks, we solve the imbalance between stitching quality and stability caused by independent optimization in traditional methods. This validates the effectiveness of end-to-end joint training. 2. Relative coordinate-based Markov smoothing strategy: To address the cumulative error flaw in absolute trajectory modeling, we design a relative coordinate-based Markov smoothing strategy. By incrementally modeling motion and applying historical path constraints, the motion relationship between adjacent frames is transformed into a relative coordinate optimization problem, significantly reducing error accumulation in long sequences and effectively ensuring the spatiotemporal continuity of the video. 3. Dynamic attention mask mechanism: To mitigate the disparity interference caused by dynamic objects, we propose a dynamic attention mask mechanism. By combining foreground motion prediction to generate spatiotemporal weight maps and using L1 photometric loss to dynamically suppress misalignment interference in dynamic regions, the robustness of stitching in complex scenes is significantly improved. This study advances the state of online video stitching and stabilization by integrating three core innovations: end-to-end collaborative optimization architecture, a relative coordinate-based Markov smoothing strategy, and a dynamic attention mask mechanism. These contributions address shortcomings in prior phase-wise approaches and enable high-quality, low-distortion stitching under strong camera shake and dynamic content. Future efforts will focus on expanding the training set to encompass a wider variety of scene types (e.g., low-light and underwater), exploring lightweight model variants for deployment on mobile and embedded hardware and refining dynamic mask generation using more sophisticated semantic segmentation or depth cues. Such extensions aim to further enhance the generalization and real-time applicability of our framework, paving the way for seamless integration into AR/VR platforms and next-generation consumer video applications. A brief ethical consideration is warranted, as real-world use in video surveillance or drone operations may capture identifiable individuals without consent. High-quality stabilization and stitching can enable unauthorized tracking or profiling. To mitigate this, practitioners should anonymize sensitive data (e.g., blur faces or license plates), comply with applicable privacy regulations, and implement clear data governance policies. Such measures help ensure responsible deployment of the proposed framework. Conceptualization, P.W., P.Q. and J.Z. ; methodology, P.W. and R.C. ; software, P.W. ; validation, P.W. and B.H. ; formal analysis, P.W. and P.Z. ; investigation, P.W. ; resources, P.Q. ; data curation, P.W. and B.H. ; writing--original draft preparation, P.W. ; writing--review and editing, P.W., P.Q. and Z.C. ; visualization, P.W. ; supervision, P.Q., J.Z. and R.C; project administration, P.Q. ; funding acquisition, P.Q. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Major Science and Technology Project of Shanxi Province, China (Shanxi Provincial Department of Science and Technology, Grant No.