Authors:
Jakob S. Lauridsen
1
;
Julius A. G. Grassmé
1
;
Malte Pedersen
1
;
David Getreuer Jensen
2
;
Søren Holm Andersen
2
and
Thomas B. Moeslund
1
Affiliations:
1
Section of Media Technology, Aalborg University and Denmark
;
2
EnviDan, Aalborg and Denmark
Keyword(s):
Computer Vision, Circular Analogue Gauge, Gauge Reading Principal Component Analysis, Expectation Maximization, Digital Time Series, Parametric Object Classification.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Device Calibration, Characterization and Modeling
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Segmentation and Grouping
;
Shape Representation and Matching
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 probability distributions estimated using 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.