Update Video Visulization

README.md

@@ -596,7 +596,14 @@ The following visualization demonstrates D-FINE's predictions in various complex
 
 </details>
 
+<details open>
+<summary> Video </summary>
+
+We conduct object detection using D-FINE and YOLO11 on a complex street scene video from YouTube. Despite challenging conditions such as backlighting, motion blur, and dense occlusion, D-FINE-X successfully detects nearly all targets, including subtle small objects like backpacks, bicycles, and traffic lights. Its confidence scores and the localization precision for blurred edges are significantly higher than those of YOLO11.
+
+https://github.com/user-attachments/assets/d5b50dfe-ecdd-4c71-ad6a-911640de99e6
 
+</details>
 
 <!-- <div style="display: flex; flex-wrap: wrap; justify-content: center; margin: 0; padding: 0;">
     <img src="https://raw.githubusercontent.com/Peterande/storage/master/figs/merged_image.jpg" style="width:99.96%; margin: 0; padding: 0;" />

README_cn.md

@@ -581,6 +581,15 @@ FDR在检测场景中的可视化，包括初始和优化后的边界框，以
 
 </details>
 
+<details open>
+<summary> 视频 </summary>
+
+我们分别使用 D-FINE 和 YOLO11 对 YouTube 上的一段复杂街景视频进行了目标检测。尽管存在逆光、虚化模糊和密集遮挡等不利因素，D-FINE-X 依然成功检测出几乎所有目标，包括背包、自行车和信号灯等难以察觉的小目标，其置信度、以及模糊边缘的定位准确度明显高于 YOLO11x。
+
+https://github.com/user-attachments/assets/d5b50dfe-ecdd-4c71-ad6a-911640de99e6
+
+</details>
+
 <!-- <table><tr>
 <td><img src=https://raw.githubusercontent.com/Peterande/storage/master/figs/merged_image.jpg border=0 width=1000></td>
 </tr></table> -->