Background-foreground segmentation for interior sensing in automotive industry
Abstract To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification of detected instances play an important role in interior sensing. By the knowledge of the seat occupancy status...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-12-01
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| Series: | Journal of Mathematics in Industry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13362-022-00128-9 |
| _version_ | 1797973749568372736 |
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| author | Claudia Drygala Matthias Rottmann Hanno Gottschalk Klaus Friedrichs Thomas Kurbiel |
| author_facet | Claudia Drygala Matthias Rottmann Hanno Gottschalk Klaus Friedrichs Thomas Kurbiel |
| author_sort | Claudia Drygala |
| collection | DOAJ |
| description | Abstract To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification of detected instances play an important role in interior sensing. By the knowledge of the seat occupancy status, it is possible to, e.g., automate the airbag deployment control. Furthermore, the presence of a driver, which is necessary for partially automated driving cars at the automation levels two to four can be verified. In this work, we compare different statistical methods from the field of image segmentation to approach the problem of background-foreground segmentation in camera based interior sensing. In the recent years, several methods based on different techniques have been developed and applied to images or videos from different applications. The peculiarity of the given scenarios of interior sensing is, that the foreground instances and the background both contain static as well as dynamic elements. In data considered in this work, even the camera position is not completely fixed. We review and benchmark three different methods ranging, i.e., Gaussian Mixture Models (GMM), Morphological Snakes and a deep neural network, namely a Mask R-CNN. In particular, the limitations of the classical methods, GMM and Morphological Snakes, for interior sensing are shown. Furthermore, it turns, that it is possible to overcome these limitations by deep learning, e.g. using a Mask R-CNN. Although only a small amount of ground truth data was available for training, we enabled the Mask R-CNN to produce high quality background-foreground masks via transfer learning. Moreover, we demonstrate that certain augmentation as well as pre- and post-processing methods further enhance the performance of the investigated methods. |
| first_indexed | 2024-04-11T04:09:11Z |
| format | Article |
| id | doaj.art-ff8c2fed7aec4dd988b9c0cb67c5eb00 |
| institution | Directory Open Access Journal |
| issn | 2190-5983 |
| language | English |
| last_indexed | 2024-04-11T04:09:11Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Mathematics in Industry |
| spelling | doaj.art-ff8c2fed7aec4dd988b9c0cb67c5eb002023-01-01T12:14:41ZengSpringerOpenJournal of Mathematics in Industry2190-59832022-12-0112113210.1186/s13362-022-00128-9Background-foreground segmentation for interior sensing in automotive industryClaudia Drygala0Matthias Rottmann1Hanno Gottschalk2Klaus Friedrichs3Thomas Kurbiel4School of Mathematics and Science, IZMD & IMACM, University of WuppertalSchool of Mathematics and Science, IZMD & IMACM, University of WuppertalSchool of Mathematics and Science, IZMD & IMACM, University of WuppertalAptiv Services Deutschland GmbHAptiv Services Deutschland GmbHAbstract To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification of detected instances play an important role in interior sensing. By the knowledge of the seat occupancy status, it is possible to, e.g., automate the airbag deployment control. Furthermore, the presence of a driver, which is necessary for partially automated driving cars at the automation levels two to four can be verified. In this work, we compare different statistical methods from the field of image segmentation to approach the problem of background-foreground segmentation in camera based interior sensing. In the recent years, several methods based on different techniques have been developed and applied to images or videos from different applications. The peculiarity of the given scenarios of interior sensing is, that the foreground instances and the background both contain static as well as dynamic elements. In data considered in this work, even the camera position is not completely fixed. We review and benchmark three different methods ranging, i.e., Gaussian Mixture Models (GMM), Morphological Snakes and a deep neural network, namely a Mask R-CNN. In particular, the limitations of the classical methods, GMM and Morphological Snakes, for interior sensing are shown. Furthermore, it turns, that it is possible to overcome these limitations by deep learning, e.g. using a Mask R-CNN. Although only a small amount of ground truth data was available for training, we enabled the Mask R-CNN to produce high quality background-foreground masks via transfer learning. Moreover, we demonstrate that certain augmentation as well as pre- and post-processing methods further enhance the performance of the investigated methods.https://doi.org/10.1186/s13362-022-00128-9Background-foreground segmentationBackground subtractionInterior sensingGaussian mixture modelMorphological snakesMask R-CNN |
| spellingShingle | Claudia Drygala Matthias Rottmann Hanno Gottschalk Klaus Friedrichs Thomas Kurbiel Background-foreground segmentation for interior sensing in automotive industry Journal of Mathematics in Industry Background-foreground segmentation Background subtraction Interior sensing Gaussian mixture model Morphological snakes Mask R-CNN |
| title | Background-foreground segmentation for interior sensing in automotive industry |
| title_full | Background-foreground segmentation for interior sensing in automotive industry |
| title_fullStr | Background-foreground segmentation for interior sensing in automotive industry |
| title_full_unstemmed | Background-foreground segmentation for interior sensing in automotive industry |
| title_short | Background-foreground segmentation for interior sensing in automotive industry |
| title_sort | background foreground segmentation for interior sensing in automotive industry |
| topic | Background-foreground segmentation Background subtraction Interior sensing Gaussian mixture model Morphological snakes Mask R-CNN |
| url | https://doi.org/10.1186/s13362-022-00128-9 |
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