Locating people has been one of the most popular subjects in computer vision

which is the basis for human-computer interaction. Locating people and motion tracking is the inverse problem of multi-camera imaging

which is gaining videos from one or more cameras

to compute the location or motion of people in videos. In this paper

we propose two types of geometrical constraints for multi-camera approaches of locating people. The accuracy of people locating algorithms relies on the exact location in the world coordinate system during experiments

which has been a great difficulty in computer vision experiments. In this paper

we present a new people locating experimental method based on virtual data

from which we can more easily get the accurate location of people in the world coordinate system. Because of the completeness of the geometric constraints

we do not need to assume the cameras’ placement and restrict the location (occlusion) or motion of the people in the scene. Using virtual and real data in the experiments

it can be more accurate for verifying the accuracy and adaptability of the algorithm. The experimental results show that the algorithm works stable and accurate. Addition

the trajectory of the people can be acquired based on the results.