We propose a bio-inspired approach to motion estimation based on recent neuroscience findings concerning the motion pathway. Our goal is to identify the key biological features in order to reach a good compromise between bio-inspiration and computational efficiency. Here we choose the neural field formalism which provides a sound mathematical framework to describe the model at a macroscopic scale. Within this framework we define the cortical activity as coupled integro-differential equations and we prove the well-posedness of the model. We show how our model performs on some classical computer vision videos, and we compare its behaviour against the visual system on a simple classical video used in psychophysics. Following this idea, we propose a new benchmark to evaluate models against visual system performance. Baseline results are provided for both bio-inspired and computer vision models. Results confirm the good performance of recent computer vision approaches even on such synthetic stimuli, and also show that taking biology into account in models can improve performance. As a whole, this article affords a considerable insight into how biology can bring new ideas in computer vision at different levels: modelling principles, mathematical formalism and evaluation methodology. Perspectives around this work are promising and cover the addition of delays to constrain propagation as well as the extension of our benchmark to better characterise the visual system performance.