Abstract: Particle tracking methods extract high-fidelity particle-scale velocity data from digital video measurements of particle laden flow. This experimental technique is often used to better understand the motion of particles and fluids in chemical processes and other complex particulate systems. Velocimetry measurements are also commonly used as benchmark data against which computational models are validated. However, the methods, codes, and experimental setups all have limitations. It is imperative that practitioners verify the velocimetry methods and their implementation as well as understand the limitations of experimental setups. This work focuses on quantifying the visible depth of field in a dense fluidized bed. Following a precedent set by the particle imaging velocimetry community, a particle velocity field is manufactured using a computational fluid dynamics and discrete element method simulation. Photo realistic high-speed videos are rendered based on the simulated data using the three-dimensional creation software Blender. Particle velocities are extracted from the synthetic high-speed videos using three variants of Particle Tracking Velocimetry and Optical Flow Velocimetry methodologies. The tracked results are then compared to the known solution, quantifying the error associated with the assumed visible depth. The results indicate that at a depth of one particle diameter, all three particle tracking codes give accurate measurements, largely within 5%. However, the error increases when the full bed video measurements are compared to the known solution at one particle diameter, i.e., mimicking a CFD validation study. For some statistics the constant depth assumption only increases the error slightly, for others significantly.