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 is), very high dynamic range (140dB vs. 60dB), 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.

Event-based Vision: A Survey

Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.

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Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars

Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based cameras. These properties make event-based cameras an ideal choice for autonomous vehicles, robot navigation or UAV vision, among others. However, the accuracy of event-based object classification algorithms, which is of crucial importance for any reliable system working in real-world conditions, is still far behind their frame-based counterparts. Two main reasons for this performance gap are: 1. The lack of effective low-level representations and architectures for event-based object classification and 2. The absence of large real-world event-based datasets. In this paper we address both problems. First, we introduce a novel event-based feature representation together with a new machine learning architecture. Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation. Second, we release the first large real-world event-based dataset for object classification. We compare our method to the state-of-the-art with extensive experiments, showing better classification performance and real-time computation.

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HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification

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 μs), very high dynamic range (140 dB versus 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. 

Event-Based Vision: A Survey

Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training. To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras. In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input. The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network. We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks. This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the event-based domain.

EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor “frames” that consist of a constant number of DAVIS ON and OFF events. The network is thus “data driven” at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing.

Steering a predator robot using a mixed frame/event-driven convolutional neural network

Back side illumination has become standard image sensor technology owing to its superior quantum efficiency and fill factor. A direct comparison of front and back side illumination (FSI and BSI) used in event-based dynamic and active pixel vision sensors (DAVIS) is interesting because of the potential of BSI to greatly increase the small 20% fill factor of these complex pixels. This brief compares identically designed front and back illuminated DAVIS silicon retina vision sensors. They are compared in term of quantum efficiency (QE), leak activity and modulation transfer function (MTF). The BSI DAVIS achieves a peak QE of 93% compared with the FSI DAVIS, peak QE of 24%, but reduced MTF, due to pixel crosstalk and parasitic photocurrent. Significant “leak events” in the BSI DAVIS limit its use to controlled illumination scenarios without very bright light sources. Effects of parasitic photocurrent and modulation transfer functions with and without IR cut filters are also reported.

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Front and Back Illuminated Dynamic and Active Pixel Vision Sensors Comparison

This paper reports an object tracking algorithm for a moving platform using the dynamic and active-pixel vision sensor (DAVIS). It takes advantage of both the active pixel sensor (APS) frame and dynamic vision sensor (DVS) event outputs from the DAVIS. The tracking is performed in a three step-manner: regions of interest (ROIs) are generated by a cluster-based tracking using the DVS output, likely target locations are detected by using a convolutional neural network (CNN) on the APS output to classify the ROIs as foreground and background, and finally a particle filter infers the target location from the ROIs. Doing convolution only in the ROIs boosts the speed by a factor of 70 compared with full-frame convolutions for the 240×180 frame input from the DAVIS. The tracking accuracy on a predator and prey robot database reaches 90% with a cost of less than 20ms/frame in Matlab on a normal PC without using a GPU.

Combined frame- and event-based detection and tracking

This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor "frames" that consist of a constant number of DAVIS ON and OFF events. The network is thus "data driven" at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing.

Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network

Applications requiring detection of small visual contrast require high sensitivity. Event cameras can provide higher dynamic range (DR) and reduce data rate and latency, but most existing event cameras have limited sensitivity. This paper presents the results of a 180-nm Towerjazz CIS process vision sensor called SDAVIS192. It outputs temporal contrast dynamic vision sensor (DVS) events and conventional active pixel sensor frames. The SDAVIS192 improves on previous DAVIS sensors with higher sensitivity for temporal contrast. The temporal contrast thresholds can be set down to 1% for negative changes in logarithmic intensity (OFF events) and down to 3.5% for positive changes (ON events). The achievement is possible through the adoption of an in-pixel preamplification stage. This preamplifier reduces the effective intrascene DR of the sensor (70 dB for OFF and 50 dB for ON), but an automated operating region control allows up to at least 110-dB DR for OFF events. A second contribution of this paper is the development of characterization methodology for measuring DVS event detection thresholds by incorporating a measure of signal-to-noise ratio (SNR). At average SNR of 30 dB, the DVS temporal contrast threshold fixed pattern noise is measured to be 0.3%-0.8% temporal contrast. Results comparing monochrome and RGBW color filter array DVS events are presented. The higher sensitivity of SDAVIS192 make this sensor potentially useful for calcium imaging, as shown in a recording from cultured neurons expressing calcium sensitive green fluorescent protein GCaMP6f.

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Diederik Paul Moeys

Papers

Steering a predator robot using a mixed frame/event-driven convolutional neural network

Front and Back Illuminated Dynamic and Active Pixel Vision Sensors Comparison

Combined frame- and event-based detection and tracking

Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network

A Sensitive Dynamic and Active Pixel Vision Sensor for Color or Neural Imaging Applications