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

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

Parametric Dense Stereovision Implementation on a System-on Chip (SoC)

This paper proposes a novel hardware implementation of a dense recovery of stereovision 3D measurements. Traditionally 3D stereo systems have imposed the maximum number of stereo correspondences, introducing a large restriction on artificial vision algorithms. The proposed system-on-chip (SoC) provides great performance and efficiency, with a scalable architecture available for many different situations, addressing real time processing of stereo image flow. Using double buffering techniques properly combined with pipelined processing, the use of reconfigurable hardware achieves a parametrisable SoC which gives the designer the opportunity to decide its right dimension and features. The proposed architecture does not need any external memory because the processing is done as image flow arrives. Our SoC provides 3D data directly without the storage of whole stereo images. Our goal is to obtain high processing speed while maintaining the accuracy of 3D data using minimum resources. Configurable parameters may be controlled by later/parallel stages of the vision algorithm executed on an embedded processor. Considering hardware FPGA clock of 100 MHz, image flows up to 50 frames per second (fps) of dense stereo maps of more than 30,000 depth points could be obtained considering 2 Mpix images, with a minimum initial latency. The implementation of computer vision algorithms on reconfigurable hardware, explicitly low level processing, opens up the prospect of its use in autonomous systems, and they can act as a coprocessor to reconstruct 3D images with high density information in real time

CED: Color Event Camera Dataset

Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called 'events'. Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Until recently, event cameras have been limited to outputting events in the intensity channel, however, recent advances have resulted in the development of color event cameras, such as the Color-DAVIS346. In this work, we present and release the first Color Event Camera Dataset (CED), containing 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes, which we hope will help drive forward event-based vision research. We also present an extension of the event camera simulator ESIM that enables simulation of color events. Finally, we present an evaluation of three state-of-the-art image reconstruction methods that can be used to convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to visualise the event stream, and for use in downstream vision applications.Comment: Conference on Computer Vision and Pattern Recognition Workshop

Fusing Frame and Event Vision for High-speed Optical Flow for Edge Application

Optical flow computation with frame-based cameras provides high accuracy but the speed is limited either by the model size of the algorithm or by the frame rate of the camera. This makes it inadequate for high-speed applications. Event cameras provide continuous asynchronous event streams overcoming the frame-rate limitation. However, the algorithms for processing the data either borrow frame like setup limiting the speed or suffer from lower accuracy. We fuse the complementary accuracy and speed advantages of the frame and event-based pipelines to provide high-speed optical flow while maintaining a low error rate. Our bio-mimetic network is validated with the MVSEC dataset showing 19% error degradation at 4x speed up. We then demonstrate the system with a high-speed drone flight scenario where a high-speed event camera computes the flow even before the optical camera sees the drone making it suited for applications like tracking and segmentation. This work shows the fundamental trade-offs in frame-based processing may be overcome by fusing data from other modalities

Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.Comment: Accepted to IEEE SENSORS 202

Free as a Bird: Event-Based Dynamic Sense-and-Avoid for Ornithopter Robot Flight

Autonomous flight of flapping-wing robots is a major challenge for robot perception. Most of the previous sense-and-avoid works have studied the problem of obstacle avoidance for flapping-wing robots considering only static obstacles. This letter presents a fully onboard dynamic sense-and-avoid scheme for large-scale ornithopters using event cameras. These sensors trigger pixel information due to changes of illumination in the scene such as those produced by dynamic objects. The method performs event-by-event processing in low-cost hardware such as those onboard small aerial vehicles. The proposed scheme detects obstacles and evaluates possible collisions with the robot body. The onboard controller actuates over the horizontal and vertical tail deflections to execute the avoidance maneuver. The scheme is validated in both indoor and outdoor scenarios using obstacles of different shapes and sizes. To the best of the authors’ knowledge, this is the first event-based method for dynamic obstacle avoidance in a flapping-wing robot.Consejo Europeo de Investigación (ERC) 788247Comisión Europea - Proyecto AERIAL-CORE H2020-2019-871479Ministerio de Universidades FPU19/0469