Wind energy offers a sustainable solution to reduce carbon emissions but presents risks to bird populations, particularly through potential collisions with turbine blades. The Bird Protection System, BPS, employing stereo vision, aims to mitigate these risks by detecting and tracking birds and estimating their distance from turbines. The precision of distance estimation remains challenging due to quantization uncertainty and environmental factors such as lighting, background complexity, and asynchronous camera frames, which can lead to unstable and imprecise measurements. This paper explores methods to enhance the accuracy and reliability of bird tracking systems by addressing one of the key technical challenges: precise disparity estimation. We investigate three methods: based on the detected object’s bounding box center, the object’s center of gravity, and the image alignment technique that uses cross-correlation. The methods use an object’s image extracted from the background and resized to ensure a subpixel refinement. Our findings show that the center of gravity and cross-correlation methods with resizing significantly enhance tracking stability and precision, and the former is also computationally efficient, rendering it useful for real-time applications, such as BPS.