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

Duo, Jingyun; Zhao, Long

doi:10.3390/s21041475

Cited by 12 publications

(6 citation statements)

References 31 publications

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“…Its matching mechanism for adding nodes is based on spatio-temporal constraints [21]. Duo and Zhao [22] added constraints on the direction of corner points to the tree structure based on spatio-temporal constraints. This improvement can cut off branches and simplify the tree structure.…”

Section: Related Workmentioning

confidence: 99%

Enhancing robustness in asynchronous feature tracking for event cameras through fusing frame steams

Xu,

Yu,

Jin

et al. 2024

Complex Intell. Syst.

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Event cameras produce asynchronous discrete outputs due to the independent response of camera pixels to changes in brightness. The asynchronous and discrete nature of event data facilitate the tracking of prolonged feature trajectories. Nonetheless, this necessitates the adaptation of feature tracking techniques to efficiently process this type of data. In addressing this challenge, we proposed a hybrid data-driven feature tracking method that utilizes data from both event cameras and frame-based cameras to track features asynchronously. It mainly includes patch initialization, patch optimization, and patch association modules. In the patch initialization module, FAST corners are detected in frame images, providing points responsive to local brightness changes. The patch association module introduces a nearest-neighbor (NN) algorithm to filter new feature points effectively. The patch optimization module assesses optimization quality for tracking quality monitoring. We evaluate the tracking accuracy and robustness of our method using public and self-collected datasets, focusing on average tracking error and feature age. In contrast to the event-based Kanade–Lucas–Tomasi tracker method, our method decreases the average tracking error ranging from 1.3 to 29.2% and boosts the feature age ranging from 9.6 to 32.1%, while ensuring the computational efficiency improvement of 1.2–7.6%. Thus, our proposed feature tracking method utilizes the unique characteristics of event cameras and traditional cameras to deliver a robust and efficient tracking system.

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Section: Related Workmentioning

confidence: 99%

Enhancing robustness in asynchronous feature tracking for event cameras through fusing frame steams

Xu,

Yu,

Jin

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…One of the most effective ways to exploit event data is to convert event data into frame data [60]- [66], thus the event-based frame can retain spatial information. Different from the synchronous frame captured by CMOS cameras one frame after another, the event camera captures asynchronous time series data [70]- [73]. We denoted a sequence of event data as E = {(x i , y i , p i , t i ) |i ∈ n}, where x and y represent the spatial coordinates of the event.…”

Section: A Event To Framementioning

confidence: 99%

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

Zhang¹,

Wang²

2021

Preprint

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<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>

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“…For corner recognition, Duo and Zhao suggested a microscope image segmentation technique based on Harris multiscale corner detection for the purpose of visual corner recognition. Incorporating the concept of multiresolution analysis, creating a formula for gray intensity change based on wavelet transform, and determining the properties of the scale transform enable the improved Harris corner detection algorithm to be scale, rotation, and translation invariant [6]. Huang suggested a brand-new coarse to fine corner extractor to accurately and effectively extract corner events.…”

Section: Introductionmentioning

confidence: 99%

A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator

Liu

Sui

et al. 2023

Journal of Sensors

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The precise environmental parameters derived from laser radar scan data can significantly accelerate the process of real-time localization and map-matching technique. One of the research directions is autonomous navigation algorithm based on LiDAR slam. LiDAR has the advantage of having a wide range of accuracy and distance. However, due to the limited amount of LiDAR data available and the influence of sensor noise, it is easy to run into issues such as low accuracy of robot map construction or large positioning errors. At the moment, most of feature extraction algorithms employ an iterative calculation method with high computational complexity and a large amount of computation. Furthermore, due to the dependence of the fixed separation threshold, the algorithms for extracting the linear features of laser radar data are typically undersegment and oversegment. As a result, this paper proposes a radar linear feature fitting algorithm that combines adaptive clustering and corner detection operators. First, bilateral filtering is used to reduce noise and remove invalid data points. Second, the LiDAR data points are classified using adaptive threshold clustering of distance and density. The corner detection operator is applied to the classified data points to determine all possible corners then. Finally, the least square method is used to linearly fit each class and the identified corners within each class. The simulation and experimental results demonstrate that this method avoids the influence of noise points and a fixed segmentation threshold on corner point extraction effectively. The standard variance of length is 9.41 × 10 − 5 m 2 for corner feature extraction and localization in the dataset Cartographer ROS 2D Laser SLAM at Deutsches Museum. When compared to PDBS (point distance based methods) and IEPF (iterative end point fit), only about half the time is used, the accuracy of partition processing is improved by 11.6%, and the accuracy of corner detection is improved by 20.1%. The proposed algorithm can extract the corner features of data frames and linear positioning through experimental verification accurately. The features of the laser scan data that fit are more realistic. It has higher calculation efficiency and position accuracy. It ensures real-time mobile robot map construction and is appropriate for autonomous robot map algorithms developed in embedded systems.

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

Cited by 12 publications

References 31 publications

Enhancing robustness in asynchronous feature tracking for event cameras through fusing frame steams

Enhancing robustness in asynchronous feature tracking for event cameras through fusing frame steams

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

A Radar Linear Feature Fitting Algorithm Combining Adaptive Clustering and Corner Detection Operator

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