# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 6 Conclusions and Future Work  
Broadcasting a cycling race is a complex process that requires the coordinated efforts of numerous people, including cameramen, directors, and video editors. Their collective aim is to deliver a seamless live viewing experience, which requires minimal delay in processing and presenting the footage. This constraint, however, limits the complexity of visualizations, such as overlays that highlight points of interest during the race. To address these challenges and improve visualizations while reducing manual workload, a computer vision pipeline has been proposed to automatically recognize and track POIs in the helicopter stream of live race broadcasts. The POI recognition process comprises five stages, integrating a saliency detection model with SAM to propose potential objects of interest. These are then verified using a keypoint matching approach to determine which POI they represent. This methodology has demonstrated high effectiveness, achieving a precision and recall of over 75%. Once a POI is recognized, a tracker is started to follow it through the video stream. Two tracking solutions are discussed and provided within the solution to choose from. A traditional tracker, MedianFlow, is provided due to its speed and simplicity, maintaining a mean tracking completeness of approximately 95%. Although the intersection over union scores drop from the beginning to the end of the broadcasted POI time window, the tracking remains stable and usable for live visualization. A discussed limitation of this approach is the potential loss of the POI when the camera rotates around it. To improve this, the more advanced SAM 2 tracking is also available, which achieves nearly perfect segmentation tracking results for almost all POIs, even when the camera is circling around the POI. However, this tracker requires more computing power in order to work real-time. Both tracking approaches benefit from our proposed POI region refinement step that uses the POI recognition information in order to refine the mask of the object. Future research opportunities are available to refine this work. The current saliency-based method for proposing interesting regions could be replaced with a custom model specifically designed to detect objects like churches, windmills, and monuments, thereby increasing detection rates. For POI recognition, an embedding-based approach could enhance system scalability. Additionally, developing a custom tracking model tailored to POIs could improve robustness against rotational challenges at faster inference speeds. In summary, this research highlights the potential of computer vision technologies to reduce human workload in live cycling race broadcasts. Additionally, it also paves the way for more engaging visualization options, enhancing the overall viewing experience of cycling races. **Author Contributions:** Conceptualization, J.V., R.D., M.S., S.V.H. and S.V. ; methodology, J.V. ; software, J.V. ; validation, J.V. ; formal analysis, J.V. ; investigation, J.V. ; data curation, J.V. ; writing--original draft preparation, J.V. ; writing--review and editing, J.V., R.D., M.S., S.V.H. and S.V. ; visualization, J.V. ; supervision, M.S., S.V.H. and S.V. ; project administration, S.V.H. and S.V. ; funding acquisition, S.V.H. and S.V. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by imec and the Flemish Government's Department of Culture, Youth, and Media within the project called Digital Transformation Media, grant number 94186. The APC was funded by Ghent University--imec. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to confidentiality. **Acknowledgments:** We acknowledge the use of GPT-4o by OpenAI for proofreading this manuscript. **Conflicts of Interest:** The authors declare no conflicts of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: