Bird migration is a critical indicator of environmental health, biodiversity, and climate change. Existing techniques for monitoring bird migration are either expensive (e.g., satellite tracking), labor-intensive (e.g., moon watching), indirect and thus less accurate (e.g., weather radar), or intrusive (e.g., attaching geolocators on captured birds). In this paper, we present a vision-based system for detecting migrating birds in flight at night. Our system takes stereo videos of the night sky as inputs, detects multiple flying birds and estimates their orientations, speeds, and altitudes. The main challenge lies in detecting flying birds of unknown trajectories under high noise level due to the low-light environment. We address this problem by incorporating stereo constraints for rejecting physically implausible configurations and gathering evidence from two (or more) views. Specifically, we develop a robust stereo-based 3D line fitting algorithm for geometric verification and a deformable part response accumulation strategy for trajectory verification. We demonstrate the effectiveness of the proposed approach through quantitative evaluation of real videos of birds migrating at night collected with near-infrared cameras.