Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation

Rapid and low power computation of optical flow (OF) is potentially useful in robotics. The dynamic vision sensor (DVS) event camera produces quick and sparse output, and has high dynamic range, but conventional OF algorithms are frame-based and cannot be directly used with event-based cameras. Previous DVS OF methods do not work well with dense textured input and are designed for implementation in logic circuits. This paper proposes a new block-matching based DVS OF algorithm which is inspired by motion estimation methods used for MPEG video compression. The algorithm was implemented both in software and on FPGA. For each event, it computes the motion direction as one of 9 directions. The speed of the motion is set by the sample interval. Results show that the Average Angular Error can be improved by 30\% compared with previous methods. The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11 clock cycles), 20 times faster than a Java software implementation running on a desktop PC. Sample data is shown that the method works on scenes dominated by edges, sparse features, and dense texture.Comment: Published in ISCAS 201

Unraveling the paradox of intensity-dependent DVS pixel noise

Dynamic vision sensor (DVS) event camera output is affected by noise, particularly in dim lighting conditions. A theory explaining how photon and electron noise affect DVS output events has so far not been developed. Moreover, there is no clear understanding of how DVS parameters and operating conditions affect noise. There is an apparent paradox between the real noise data observed from the DVS output and the reported noise measurements of the logarithmic photoreceptor. While measurements of the logarithmic photoreceptor predict that the photoreceptor is approximately a first-order system with RMS noise voltage independent of the photocurrent, DVS output shows higher noise event rates at low light intensity. This paper unravels this paradox by showing how the DVS photoreceptor is a second-order system, and the assumption that it is first-order is generally not reasonable. As we show, at higher photocurrents, the photoreceptor amplifier dominates the frequency response, causing a drop in RMS noise voltage and noise event rate. We bring light to the noise performance of the DVS photoreceptor by presenting a theoretical explanation supported by both transistor-level simulation results and chip measurements.Comment: Presented in 2021 International Image Sensor Workshop (IISW

EDFLOW: Event Driven Optical Flow Camera with Keypoint Detection and Adaptive Block Matching

Event cameras such as the Dynamic Vision Sensor (DVS) are useful because of their low latency, sparse output, and high dynamic range. In this paper, we propose a DVS+FPGA camera platform and use it to demonstrate the hardware implementation of event-based corner keypoint detection and adaptive block-matching optical flow. To adapt sample rate dynamically, events are accumulated in event slices using the area event count slice exposure method. The area event count is feedback controlled by the average optical flow matching distance. Corners are detected by streaks of accumulated events on event slice rings of radius 3 and 4 pixels. Corner detection takes about 6 clock cycles (16 MHz event rate at the 100MHz clock frequency) At the corners, flow vectors are computed in 100 clock cycles (1 MHz event rate). The multiscale block match size is 25x25 pixels and the flow vectors span up to 30-pixel match distance. The FPGA processes the sum-of-absolute distance block matching at 123 GOp/s, the equivalent of 1230 Op/clock cycle. EDFLOW is several times more accurate on MVSEC drone and driving optical flow benchmarking sequences than the previous best DVS FPGA optical flow implementation, and achieves similar accuracy to the CNN-based EV-Flownet, although it burns about 100 times less power. The EDFLOW design and benchmarking videos are available at https://sites.google.com/view/edflow21/home

DDD17: End-To-End DAVIS Driving Dataset

Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events. The APS stream is a sequence of standard grayscale global-shutter image sensor frames. The DVS events represent brightness changes occurring at a particular moment, with a jitter of about a millisecond under most lighting conditions. They have a dynamic range of >120 dB and effective frame rates >1 kHz at data rates comparable to 30 fps (frames/second) image sensors. To overcome some of the limitations of current image acquisition technology, we investigate in this work the use of the combined DVS and APS streams in end-to-end driving applications. The dataset DDD17 accompanying this paper is the first open dataset of annotated DAVIS driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. As an example application, we performed a preliminary end-to-end learning study of using a convolutional neural network that is trained to predict the instantaneous steering angle from DVS and APS visual data.Comment: Presented at the ICML 2017 Workshop on Machine Learning for Autonomous Vehicle

The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM

New vision sensors, such as the Dynamic and Active-pixel Vision sensor (DAVIS), incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array. These sensors have great potential for high-speed robotics and computer vision because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and very high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called "events") and synchronous grayscale frames. For this purpose, we present and release a collection of datasets captured with a DAVIS in a variety of synthetic and real environments, which we hope will motivate research on new algorithms for high-speed and high-dynamic-range robotics and computer-vision applications. In addition to global-shutter intensity images and asynchronous events, we provide inertial measurements and ground-truth camera poses from a motion-capture system. The latter allows comparing the pose accuracy of ego-motion estimation algorithms quantitatively. All the data are released both as standard text files and binary files (i.e., rosbag). This paper provides an overview of the available data and describes a simulator that we release open-source to create synthetic event-camera data.Comment: 7 pages, 4 figures, 3 table

Delta Networks for Optimized Recurrent Network Computation

Many neural networks exhibit stability in their activation patterns over time in response to inputs from sensors operating under real-world conditions. By capitalizing on this property of natural signals, we propose a Recurrent Neural Network (RNN) architecture called a delta network in which each neuron transmits its value only when the change in its activation exceeds a threshold. The execution of RNNs as delta networks is attractive because their states must be stored and fetched at every timestep, unlike in convolutional neural networks (CNNs). We show that a naive run-time delta network implementation offers modest improvements on the number of memory accesses and computes, but optimized training techniques confer higher accuracy at higher speedup. With these optimizations, we demonstrate a 9X reduction in cost with negligible loss of accuracy for the TIDIGITS audio digit recognition benchmark. Similarly, on the large Wall Street Journal speech recognition benchmark even existing networks can be greatly accelerated as delta networks, and a 5.7x improvement with negligible loss of accuracy can be obtained through training. Finally, on an end-to-end CNN trained for steering angle prediction in a driving dataset, the RNN cost can be reduced by a substantial 100X

Feedback control of event cameras

Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events. Event production at the pixel level is controlled by threshold, bandwidth, and refractory period bias current parameter settings. Biases must be adjusted to match application requirements and the optimal settings depend on many factors. As a first step towards automatic control of biases, this paper proposes fixed-step feedback controllers that use measurements of event rate and noise. The controllers regulate the event rate within an acceptable range using threshold and refractory period control, and regulate noise using bandwidth control. Experiments demonstrate model validity and feedback control

Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization

The operation of the Dynamic Vision Sensor (DVS) event camera is controlled by the user through adjusting different bias parameters. These biases affect the response of the camera by controlling - among other parameters - the bandwidth, sensitivity, and maximum firing rate of the pixels. Besides determining the response of the camera to input signals, biases significantly impact its noise performance. Bias optimization is a multivariate process depending on the task and the scene, to which the user’s knowledge about pixel design and non-idealities can be of great importance.In this paper, we go step-by-step along the signal pathway of the DVS pixel, shining light on its low-level operation and non-idealities, comparing pixel level measurements with array level measurements, and discussing how biasing and illumination affect the pixel’s behavior. With the results and discussion presented, we aim to help DVS users achieve more hardware-aware camera utilization and modelling