Dynamic Resource-aware Corner Detection for Bio-inspired Vision Sensors

by   Sherif A. S. Mohamed, et al.

Event-based cameras are vision devices that transmit only brightness changes with low latency and ultra-low power consumption. Such characteristics make event-based cameras attractive in the field of localization and object tracking in resource-constrained systems. Since the number of generated events in such cameras is huge, the selection and filtering of the incoming events are beneficial from both increasing the accuracy of the features and reducing the computational load. In this paper, we present an algorithm to detect asynchronous corners from a stream of events in real-time on embedded systems. The algorithm is called the Three Layer Filtering-Harris or TLF-Harris algorithm. The algorithm is based on an events' filtering strategy whose purpose is 1) to increase the accuracy by deliberately eliminating some incoming events, i.e., noise, and 2) to improve the real-time performance of the system, i.e., preserving a constant throughput in terms of input events per second, by discarding unnecessary events with a limited accuracy loss. An approximation of the Harris algorithm, in turn, is used to exploit its high-quality detection capability with a low-complexity implementation to enable seamless real-time performance on embedded computing platforms. The proposed algorithm is capable of selecting the best corner candidate among neighbors and achieves an average execution time savings of 59 the conventional Harris score. Moreover, our approach outperforms the competing methods, such as eFAST, eHarris, and FA-Harris, in terms of real-time performance, and surpasses Arc* in terms of accuracy.



There are no comments yet.


page 1

page 4

page 6

page 7


A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems

Deep learning inference that needs to largely take place on the 'edge' i...

SE-Harris and eSUSAN: Asynchronous Event-Based Corner Detection Using Megapixel Resolution CeleX-V Camera

Event cameras are novel neuromorphic vision sensors with ultrahigh tempo...

Keypoint-based object tracking and localization using networks of low-power embedded smart cameras

Object tracking and localization is a complex task that typically requir...

Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application

Real-time apple detection in orchards is one of the most effective ways ...

FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras

Recently, the emerging bio-inspired event cameras have demonstrated pote...

luvHarris: A Practical Corner Detector for Event-cameras

There have been a number of corner detection methods proposed for event ...

Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers

Neuromorphic vision is a bio-inspired technology that has triggered a pa...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.