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, 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.

Generalization capabilities of conditional GAN for turbulent flow under changes of geometry

“…with s(ξ, t) the pixel value of the binary image and (ξ, t) an adaptive threshold calculated at each pixel ξ individually by computing the cross-correlation with a Gaussian window [30] over the neighbourhood of ξ minus a constant C. The neighbourhood is defined by a quadratic structuring element [31] of size 15 × 15 pixels with ξ in the center and C = V Z (0, t) is the gray scale of the upper left corner. Using the normalized values of s(ξ, t) we evaluate the moving average ψ(x) = x ξ at pixel ξ expressed by 1…”

Generalization Capabilities of Conditional Gan for Turbulent Flow Under Changes of Geometry

“…with s(ξ, t) the pixel value of the binary image and ϵ(ξ, t) an adaptive threshold calculated at each pixel ξ individually by computing the cross-correlation with a Gaussian window [30] over the neighbourhood of ξ minus a constant C. The neighbourhood is defined by a quadratic structuring element [31] of size 15 × 15 pixels with ξ in the center and C = V Z (0, t) is the gray scale of the upper left corner. Using the normalized values of s(ξ, t) we evaluate the moving average ψ(x) = x ξ at pixel ξ expressed by 1…”