Velocity and Color Estimation Using Event-Based Clustering

Event-based clustering provides a low-power embedded solution for low-level feature extraction in a scene. The algorithm utilizes the non-uniform sampling capability of event-based image sensors to measure local intensity variations within a scene. Consequently, the clustering algorithm forms simila...

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Published inSensors (Basel, Switzerland) Vol. 23; no. 24; p. 9768
Main Authors Lesage, Xavier, Tran, Rosalie, Mancini, Stéphane, Fesquet, Laurent
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 11.12.2023
MDPI
Subjects
Algorithms
Cameras
Case studies
Clustering
Computer Science
Computer Vision and Pattern Recognition
Datasets
embedded sensing
Embedded systems
event intensity
event polarity
event-based processing
Image Processing
low-power tracking
Sensors
Velocity
velocity estimation
color estimation
event polarity
embedded sensing
velocity estimation
event intensity
low-power tracking
event-based processing
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Summary:Event-based clustering provides a low-power embedded solution for low-level feature extraction in a scene. The algorithm utilizes the non-uniform sampling capability of event-based image sensors to measure local intensity variations within a scene. Consequently, the clustering algorithm forms similar event groups while simultaneously estimating their attributes. This work proposes taking advantage of additional event information in order to provide new attributes for further processing. We elaborate on the estimation of the object velocity using the mean motion of the cluster. Next, we are examining a novel form of events, which includes intensity measurement of the color at the concerned pixel. These events may be processed to estimate the rough color of a cluster, or the color distribution in a cluster. Lastly, this paper presents some applications that utilize these features. The resulting algorithms are applied and exercised thanks to a custom event-based simulator, which generates videos of outdoor scenes. The velocity estimation methods provide satisfactory results with a trade-off between accuracy and convergence speed. Regarding color estimation, the luminance estimation is challenging in the test cases, while the chrominance is precisely estimated. The estimated quantities are adequate for accurately classifying objects into predefined categories.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23249768