Background <p>Quantification of locomotion is central to the study of animal movement ecology. Although technological advances have enabled researchers to acquire high-resolution kinematic data, the associated methods often require multiple cameras and complicate the analysis process. Quantifying complex animal locomotion in three-dimensional space lacks an accurate, user-friendly method.</p> Methods <p>By combining deep learning tools and the pinhole camera model, we develop a novel method for reconstructing three-dimensional animal motion trajectories from monocular videos and analyzing kinematic data. We tested spatial precision and occlusion robustness in both aerial-based and ground-based scenarios. Subsequently, the method was applied to a bat-predation biomechanics study to demonstrate its capabilities. The application is based on low-cost single camera and does not require multiple devices or precise calibration.</p> Results <p>Our method rapidly reconstructs 3D trajectories for various animal movements, including flight, walking, and preying. The estimated 3D coordinates have an average bias of 0.09 m for aerial motion and 0.044 m for ground motion. Moreover, our method is extremely robust in distance estimation when faced with foreground occlusion. We extracted kinematic parameters from the 3D trajectory and gait frequencies from pixel area changes. Applying these parameters to biomechanical analysis, the results show that the obtained parameters can accurately describe the animal’s movement.</p> Conclusions <p>This lightweight and cost-effective approach allows the analysis of animal locomotion in the natural environment. It also allows researchers to flexibly adapt it to their specific needs, facilitating intelligent monitoring of the wild animals and enhancing the understanding of their locomotion data.</p>