Abstract: Methods, systems and processor-readable media for side window detection in near-infrared (NIR) images utilizing machine learning. An image-capturing unit can capture an image/video in a near-infrared (NIR) band via a side window of an incoming vehicle. A deformable part model can be generated utilizing a side window detection and B-frame detection in order to obtain a set of candidate side-windows. Side window detection can be performed based on a mixture of a tree model and a shared pool and can be globally optimized with dynamic programming and still-capture to detect the backseat side window boundary utilizing a B-pillar. A false alarm with respect to the deformable part model can be removed utilizing a super pixel generation and a longest-line detection unit in order to generate a refined deformable part model.

Abstract: A system and method for detecting a seat belt violation in a vehicle. The method may include receiving an image including a region of interest where a seat belt is visible if the seat belt is buckled. One or more first features may be extracted from the image. The one or more first features may be compared to one or more second features extracted from a plurality of sample images. It may be determined whether the seat belt is buckled in the image based upon the comparison.

Abstract: A system and method for detecting a seat belt violation in a vehicle. The method may include receiving an image including a region of interest where a seat belt is visible if the seat belt is buckled. One or more first features may be extracted from the image. The one or more first features may be compared to one or more second features extracted from a plurality of sample images. It may be determined whether the seat belt is buckled in the image based upon the comparison.

Abstract: Methods, systems and processor-readable media for side window detection in near-infrared (NIR) images utilizing machine learning. An image-capturing unit can capture an image/video in a near-infrared (NIR) band via a side window of an incoming vehicle. A deformable part model can be generated utilizing a side window detection and B-frame detection in order to obtain a set of candidate side-windows. Side window detection can be performed based on a mixture of a tree model and a shared pool and can be globally optimized with dynamic programming and still-capture to detect the backseat side window boundary utilizing a B-pillar. A false alarm with respect to the deformable part model can be removed utilizing a super pixel generation and a longest-line detection unit in order to generate a refined deformable part model.

Abstract: A method for processing image data for segmentation includes receiving image data. One or more seed points are identified within the image data. Intensity and texture features are computer based on the received image data and the seed points. The image data is represented as a graph wherein each pixel of the image data is represented as a node and edges connect nodes representative of proximate pixels of the image data and establishing edge weights for the edges of the graph using a classifier that takes as input, one or more of the computed image features. Graph-based segmentation such as segmentation using the random walker approach may then be performed based on the graph representing the image data.

Abstract: A method for processing image data for segmentation includes receiving image data. One or more seed points are identified within the image data. Intensity and texture features are computer based on the received image data and the seed points. The image data is represented as a graph wherein each pixel of the image data is represented as a node and edges connect nodes representative of proximate pixels of the image data and establishing edge weights for the edges of the graph using a classifier that takes as input, one or more of the computed image features. Graph-based segmentation such as segmentation using the random walker approach may then be performed based on the graph representing the image data.