Reading circular analogue gauges using digital image processing

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Abstract

This paper presents an image processing based pipeline for automated recognition and translation of pointer movement in analogue circular gauges. The proposed method processes an input video frame-wise in a module based manner. Noise is minimized in each image using a bilateral filter before a Gaussian mean adaptive threshold is applied to segment objects. Subsequently, the objects are described by a set of proposed features and classified using a naive Bayes classifier trained by Expectation Maximization. The pointer is classified by the Mahalanobis distance and the angle of the pointer is determined using PCA. The output is a low pass filtered digital time series based on the temporal estimations of the pointer angle. Seven test videos have been processed by the algorithm showing promising results. Both source code and video data are publicly available.
Keywords: Computer Vision, Circular Analogue Gauge, Gauge Reading Principal Component Analysis, Expectation Maximization, Digital Time Series, Parametric Object Classification.
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This paper presents an image processing based pipeline for automated recognition and translation of pointer movement in analogue circular gauges. The proposed method processes an input video frame-wise in a module based manner. Noise is minimized in each image using a bilateral filter before a Gaussian mean adaptive threshold is applied to segment objects. Subsequently, the objects are described by a set of proposed features and classified using a naive Bayes classifier trained by Expectation Maximization. The pointer is classified by the Mahalanobis distance and the angle of the pointer is determined using PCA. The output is a low pass filtered digital time series based on the temporal estimations of the pointer angle. Seven test videos have been processed by the algorithm showing promising results. Both source code and video data are publicly available.
Keywords: Computer Vision, Circular Analogue Gauge, Gauge Reading Principal Component Analysis, Expectation Maximization, Digital Time Series, Parametric Object Classification.
Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Computer Vision Theory and Applications
Publication date2019
Publication statusAccepted/In press - 2019
Publication categoryResearch
Peer-reviewedYes
ID: 292793984