To address the scale variation challenges during target handoff in dual-camera systems, this paper proposes a dual-field-of-view target handoff method based on feature association. The approach initially localizes the target in the switched field of view using a homography matrix and then employs an optimized YOLOv5 object detection network to search for candidate targets. Finally, this approach uses an enhanced OSNet network for feature association. To improve the accuracy of target handoff, the loss function of YOLOv5 was optimized. Additionally, the bottleneck attention module and cosine distance metric were introduced into OSNet. Experimental results on the CrowdHuman and Market-1501 datasets indicate that the optimized YOLOv5 network increases the average precision by 1.0 percentage point, achieving a precision of 38.5%. The mean average precision of the improved OSNet network increases by 5.4 percentage point, reaching 68.1%. When deployed on the Rockchip RK3399Pro embedded platform, equipped with 60 frame/s cameras of resolutions 1600 × 1200 and focal lengths of 35 mm and 8 mm, respectively, this approach accurately completes the target handoff within 14 frames, demonstrating the feasibility and stability of the proposed method in real-world surveillance scenarios.