Abstract
The robustness of the human visual system recovering motion estimation in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, efficiently, and effortlessly. There is obviously a great deal we can learn from our own visual system. Currently, there are several optical flow algorithms, although none of them deals efficiently with noise, illumination changes, second-order motion, occlusions, and so on. The main contribution of this work is the efficient implementation of a biologically inspired motion algorithm that borrows nature templates as inspiration in the design of architectures and makes use of a specific model of human visual motion perception: Multichannel Gradient Model (McGM). This novel customizable architecture of a neuromorphic robust optical flow can be constructed with FPGA or ASIC device using properties of the cortical motion pathway, constituting a useful framework for building future complex bioinspired systems running in real time with high computational complexity. This work includes the resource usage and performance data, and the comparison with actual systems. This hardware has many application fields like object recognition, navigation, or tracking in difficult environments due to its bioinspired and robustness properties.
Highlights
Bioinspired systems emulate the behavior of biological ones
Since we are working with synthetic sequences, we can estimate the error without any ambiguity
We have developed an FPGA-based implementation of a bioinspired robust motion estimation system with an associated complexity higher than those found in other gradientbased models commonly used in the literature
Summary
Bioinspired systems emulate the behavior of biological ones. Neuromorphic approximations [1] are based on the way how the nervous systems create physical architectures and computations, attending to the morphology, information coding, robustness against damage, and so on. If bioinspirational behavior is required, that is, ability to detect correct motion related to optical illusions or avoiding operations like matrix inverse or iterative methods that are not biologically justified, we have to select carefully a model that carries out this kind of requirements. This is the Multichannel Gradient Model (McGM) [9,10,11,12]. The stages of McGM model are explained very briefly; after that, we tackle
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