In ornithological video monitoring tasks aimed at studying bird behavior, estimating population size, and tracking migration routes, a major challenge is the robust tracking of targets under global camera motion. Such motion, often caused by pan-tilt or mobile platforms, introduces significant distortions in the optical flow. At the same time, the tracked objects are typically small, low-contrast, and highly dynamic, which considerably reduces the robustness of conventional tracking methods. This study aims to develop and experimentally validate a tracking method that can operate in video sequences affected by global motion, while maintaining high accuracy and real-time performance. The proposed approach integrates a neural network-based tracker and trajectory prediction using a Kalman filter. The method was evaluated on a dataset simulating real ornithological monitoring scenarios, including highly detailed and dynamic backgrounds, moving cameras, variable lighting, and complex object trajectories. Experimental results showed that the tracking failure rate did not exceed 5×10-4, while the average processing speed reached 21 frames per second. Compared to a conventional tracking method based on HOG+KCF and Kalman filtering, the proposed method achieved a 4-fold reduction in tracking failure rate and a 2.5-fold reduction in tracking failures under occlusion conditions. The developed method is designed for use in bird monitoring systems operating in natural and agricultural landscapes, where reliable object tracking is required in visually complex environments. The results demonstrate the potential of the proposed solution for both scientific and ornithological research, as well as applied environmental monitoring tasks. © 2025 FRUCT Oy.