In this study, light field imaging theory and three-dimensional (3D) particle tracking velocimetry (PTV) are combined to evaluate a 3D flow field using a single camera. Further, a relation is derived between the depth and the optimal refocusing coefficient based on the Gaussian optics and the similarity principle. Subsequently, the light field calibration and flow field measurement systems are established. A depth calibration method is proposed based on the theoretical model of light field imaging. When compared with the Taylor polynomial fitting method, the proposed method is proved to have high robustness. An all-in-focus image is obtained based on the principle of maximum sharpness. The particles in the all-in-focus image are positioned using the corner detection algorithm based on the minimum eigenvalue; further, the 3D velocities of the particles are obtained using the 3D PTV technology. A processing flow is established for the light field images and applied to the flow field measurement on a back step. The results prove that the light-field-imaging-based PTV technology can reconstruct the volumetric flow field.