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Related Experiment Video

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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

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Velocity and Color Estimation Using Event-Based Clustering.

Xavier Lesage^1,2, Rosalie Tran¹, Stéphane Mancini¹

¹Univ. Grenoble Alpes, CNRS (National Centre for Scientific Research), Grenoble INP (Institute of Engineering), TIMA (Techniques of Informatics and Microelectronics for Integrated Systems Architecture), F-38000 Grenoble, France.

Sensors (Basel, Switzerland)

|December 23, 2023

Summary

This summary is machine-generated.

Event-based clustering extracts scene features using event sensors. New attributes like object velocity and color improve object classification, offering a low-power embedded solution.

Keywords:

color estimation embedded sensing event intensity event polarity event-based processing low-power tracking velocity estimation

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Area of Science:

Computer Vision
Embedded Systems
Sensor Technology

Background:

Event-based sensors offer low-power, high-temporal-resolution data capture.
Traditional clustering algorithms can be adapted for event-based data.
Extracting richer attributes from event data is crucial for advanced applications.

Purpose of the Study:

To enhance event-based clustering by incorporating novel event attributes.
To estimate object velocity and color information from event streams.
To demonstrate the utility of these enhanced features in object classification.

Main Methods:

Utilized event-based image sensors for non-uniform sampling and intensity variation measurement.
Developed algorithms for clustering similar events and estimating attributes like motion and color.
Employed a custom event-based simulator generating outdoor scene videos for testing.

Main Results:

Velocity estimation achieved satisfactory results, balancing accuracy and convergence speed.
Chrominance estimation was precise, while luminance estimation presented challenges.
The enhanced features proved adequate for accurate object classification into predefined categories.

Conclusions:

Event-based clustering with added attributes provides a viable low-power solution for feature extraction.
Object velocity and color estimation from event data show promise for real-world applications.
Further research can refine luminance estimation and explore diverse application scenarios.