Measuring erythrocyte velocity in individual microvessels has important applications for biomedical and functional imaging. Recent multiphoton fluorescence microscopy approaches require injecting fluorescent tracers; moreover, only one or few vessels can be imaged at a time. To overcome these shortcomings, we used CCD-based optical imaging of intrinsic absorption changes in macroscopic vascular networks to record erythrocytes' trajectories over several mm2 of cortical surface. We then demonstrate the feasibility of erythrocyte velocity estimation from such wide-field data, using two robust, independent, algorithms. The first one is a recently published Radon transform-based algorithm that estimates erythrocyte velocity locally. We adapt it to data obtained in wide-field imaging and show, for the first time, its performance on such datasets. The second ("fasttrack") algorithm is novel. It is based on global energy minimization techniques to estimate the full spatiotemporal erythrocytes' trajectories inside vessels. We test the two algorithms on both simulated and biological data, obtained in rat cerebral cortex in a spreading depression experiment. On vessels with medium-slow erythrocyte velocities both algorithms performed well, allowing their usage as benchmark one for another. However, our novel fasttrack algorithm outperformed the other one for higher velocities, as encountered in the arterial network.