Neuromorphic Computing of Event-Based Data for High-Speed Vision-Based Navigation

Neuromorphic Computing of Event-Based Data for High-Speed Vision-Based Navigation

Paredes Valles, Fede (TU Delft Aerospace Engineering; TU Delft Control & Simulation)

de Croon, G.C.H.E. (mentor)
Scheper, K.Y.W. (mentor)

Delft University of Technology

Aerospace Engineering

2018-06-19

The combination of Spiking Neural Networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This thesis presents, to the best of the author’s knowledge, the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in a biologically plausible unsupervised fashion from the stimuli generated with an event-based camera. A novel adaptive neuron model and Spike-Timing-Dependent Plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively. After convergence, the neural architecture exhibits the main properties of biological visual motion systems: feature extraction and local and global motion perception. To assess the outcome of the learning, a shallow conventional Artificial Neural Network is trained to map the activation traces of the penultimate layer to the optical flow visual observables of ventral flows. The proposed solution is validated for simulated event sequences with ground truth measurements. Experimental results show that accurate estimates of these parameters can be obtained over a wide range of speeds.

Event-based vision
Neuromorphic
Optical flow
Spiking Neural Networks
Spike-Timing-Dependent Plasticity

http://resolver.tudelft.nl/uuid:aa13959b-79b9-4dfc-b5e0-7c501d9d3e2f

Student theses

master thesis

© 2018 Fede Paredes Valles