scholarly journals An Asynchronous Real-Time Corner Extraction and Tracking Algorithm for Event Camera

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1475 ◽  
Author(s):  
Jingyun Duo ◽  
Long Zhao
Keyword(s):  
Real Time ◽  
Dynamic Range ◽  
Computation Time ◽  
High Dynamic Range ◽  
Tracking Algorithm ◽  
Corner Detection ◽  
High Temporal Resolution ◽  
Detection And Tracking ◽  
Event Camera ◽  
Event Based

Event cameras have many advantages over conventional frame-based cameras, such as high temporal resolution, low latency and high dynamic range. However, state-of-the-art event- based algorithms either require too much computation time or have poor accuracy performance. In this paper, we propose an asynchronous real-time corner extraction and tracking algorithm for an event camera. Our primary motivation focuses on enhancing the accuracy of corner detection and tracking while ensuring computational efficiency. Firstly, according to the polarities of the events, a simple yet effective filter is applied to construct two restrictive Surface of Active Events (SAEs), named as RSAE+ and RSAE−, which can accurately represent high contrast patterns; meanwhile it filters noises and redundant events. Afterwards, a new coarse-to-fine corner extractor is proposed to extract corner events efficiently and accurately. Finally, a space, time and velocity direction constrained data association method is presented to realize corner event tracking, and we associate a new arriving corner event with the latest active corner that satisfies the velocity direction constraint in its neighborhood. The experiments are run on a standard event camera dataset, and the experimental results indicate that our method achieves excellent corner detection and tracking performance. Moreover, the proposed method can process more than 4.5 million events per second, showing promising potential in real-time computer vision applications.

scholarly journals The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM

2017 ◽  
Vol 36 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Elias Mueggler ◽  
Henri Rebecq ◽  
Guillermo Gallego ◽  
Tobi Delbruck ◽  
Davide Scaramuzza
Keyword(s):  
Computer Vision ◽  
High Speed ◽  
Dynamic Range ◽  
Ground Truth ◽  
High Dynamic Range ◽  
High Temporal Resolution ◽  
Event Camera ◽  
High Dynamic ◽  
Event Based ◽  
New Algorithms

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.

scholarly journals AFOD: An Adaptable Framework for Object Detection in Event-based Vision

2021 ◽  
Author(s):  
Shixiong Zhang ◽  
Wenmin Wang
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Temporal Resolution ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Motion Blur ◽  
High Temporal Resolution ◽  
High Dynamic ◽  
Event Based ◽  
Conventional Computer

<div>Event-based vision is a novel bio-inspired vision that has attracted the interest of many researchers. As a neuromorphic vision, the sensor is different from the traditional frame-based cameras. It has such advantages that conventional frame-based cameras can’t match, e.g., high temporal resolution, high dynamic range(HDR), sparse and minimal motion blur. Recently, a lot of computer vision approaches have been proposed with demonstrated success. However, there is a lack of some general methods to expand the scope of the application of event-based vision. To be able to effectively bridge the gap between conventional computer vision and event-based vision, in this paper, we propose an adaptable framework for object detection in event-based vision.</div>

scholarly journals AFOD: An Adaptable Framework for Object Detection in Event-based Vision

2021 ◽  
Author(s):  
Shixiong Zhang ◽  
Wenmin Wang
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Temporal Resolution ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Motion Blur ◽  
High Temporal Resolution ◽  
High Dynamic ◽  
Event Based ◽  
Conventional Computer

<div>Event-based vision is a novel bio-inspired vision that has attracted the interest of many researchers. As a neuromorphic vision, the sensor is different from the traditional frame-based cameras. It has such advantages that conventional frame-based cameras can’t match, e.g., high temporal resolution, high dynamic range(HDR), sparse and minimal motion blur. Recently, a lot of computer vision approaches have been proposed with demonstrated success. However, there is a lack of some general methods to expand the scope of the application of event-based vision. To be able to effectively bridge the gap between conventional computer vision and event-based vision, in this paper, we propose an adaptable framework for object detection in event-based vision.</div>

Event-based tracking of human hands

Sensor Review ◽  
2021 ◽  
Vol 41 (4) ◽  
pp. 382-389
Author(s):  
Laura Duarte ◽  
Mohammad Safeea ◽  
Pedro Neto
Keyword(s):  
Dynamic Range ◽  
Estimation Error ◽  
Computational Cost ◽  
Motion Blur ◽  
Hand Position ◽  
Single Event ◽  
Content Type ◽  
Event Camera ◽  
Event Based ◽  
Human Hands

Purpose This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting three-dimensional (3D) hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human–robot interactions and in obstacle avoidance for human–robot safety applications. Design/methodology/approach Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Findings Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Originality/value Tracking of human hands in 3 D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space).

scholarly journals Single-shot cross-correlator for pulse-contrast characterization of high peak-power lasers

2018 ◽  
Vol 6 ◽  
Author(s):  
Jingui Ma ◽  
Peng Yuan ◽  
Jing Wang ◽  
Guoqiang Xie ◽  
Heyuan Zhu ◽  
...  
Keyword(s):  
Real Time ◽  
Peak Power ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
System Optimization ◽  
High Peak ◽  
High Peak Power ◽  
Single Shot ◽  
High Dynamic ◽  
Pulse Contrast

Pulse contrast is a crucial parameter of high peak-power lasers since the prepulse noise may disturb laser–plasma interactions. Contrast measurement is thus a prerequisite to tackle the contrast challenge in high peak-power lasers. This paper presents the progress review of single-shot cross-correlator (SSCC) for real-time contrast characterization. We begin with the key technologies that enable an SSCC to simultaneously possess high dynamic range ($10^{10}$), large temporal window (50–70 ps) and high fidelity. We also summarize the instrumentation of SSCC prototypes and their applications on five sets of petawatt laser facilities in China. Finally, we discuss how to extend contrast measurements from time domain to spatiotemporal domain. Real-time and high-dynamic-range contrast measurements, provided by SSCC, can not only characterize various complex noises in high peak-power lasers but also guide the system optimization.

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