Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Learning of somatosensory representations for texture discrimination using a temporal coherence principle

In order to perform appropriate actions, animals need to quickly and reliably classify their sensory input. How can representations suitable for classification be acquired from statistical properties of the animal’s natural environment? Akin to behavioural studies in rats, we investigate this question using texture discrimination by the vibrissae system as a model. To account for the rat’s active sensing behaviour, we record whisker movements in a hardware model. Based on these signals, we determine the response of primary neurons, modelled as spatio-temporal filters. Using their output, we train a second layer of neurons to optimise a temporal coherence objective function. The performance in classifying textures using a single cell strongly correlates with the cell’s temporal coherence; hence output cells outperform primary cells. Using a simple, unsupervised classifier, the performance on the output cell population is same as if using a sophisticated supervised classifier on the primary cells. Our results demonstrate that the optimisation of temporal coherence yields a representation that facilitates subsequent classification by selectively conveying relevant information