Machine learning approach for detecting mobile phone usage by a driver
First Claim
1. A method for detecting electronic device use by a driver of a vehicle, the method comprising:
- acquiring a monochrome NIR image including a vehicle from an associated image capture device positioned to view oncoming traffic;
locating a region of the vehicle in the monochrome NIR image;
processing pixels of the located region of the monochrome NIR image for computing a feature vector describing a windshield region of the vehicle;
applying the feature vector to a classifier for classifying the monochrome NIR image into respective classes including at least classes for candidate electronic device use and candidate electronic device non-use; and
,outputting the classification;
wherein processing the image includes generating a global descriptor describing the entire image using a process selected from a group consisting of;
a Successive Mean Quantization Transform (SMQT);
a Scale-Invariant Feature Transform (SIFT);
a Histogram of Gradients (HOG);
a Bag-of-Visual-Words Representation;
a Fisher Vector(FV) Representation; and
,a combination of the above;
wherein the SMQT process includes determining a feature vector for each pixel in the image by analyzing adjacent pixels in the region of the pixel, wherein for each pixel, the pixel is designated as a center pixel in a region of adjacent pixels, an average value for the pixels in the region is determined, the average value is set as a threshold value, the value of each pixel in the region is compared to the threshold, each pixel in the region is assigned a binary value based on the comparison, and a binary number is generated for the region.
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Abstract
A system and method for detecting electronic device use by a driver of a vehicle including acquiring an image including a vehicle from an associated image capture device positioned to view oncoming traffic, locating a windshield region of the vehicle in the captured image, processing pixels of the windshield region of the image for computing a feature vector describing the windshield region of the vehicle, applying the feature vector to a classifier for classifying the image into respective classes including at least classes for candidate electronic device use and candidate electronic device non-use, and outputting the classification.
28 Citations
17 Claims
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1. A method for detecting electronic device use by a driver of a vehicle, the method comprising:
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acquiring a monochrome NIR image including a vehicle from an associated image capture device positioned to view oncoming traffic; locating a region of the vehicle in the monochrome NIR image; processing pixels of the located region of the monochrome NIR image for computing a feature vector describing a windshield region of the vehicle; applying the feature vector to a classifier for classifying the monochrome NIR image into respective classes including at least classes for candidate electronic device use and candidate electronic device non-use; and
,outputting the classification; wherein processing the image includes generating a global descriptor describing the entire image using a process selected from a group consisting of; a Successive Mean Quantization Transform (SMQT); a Scale-Invariant Feature Transform (SIFT); a Histogram of Gradients (HOG); a Bag-of-Visual-Words Representation; a Fisher Vector(FV) Representation; and
,a combination of the above; wherein the SMQT process includes determining a feature vector for each pixel in the image by analyzing adjacent pixels in the region of the pixel, wherein for each pixel, the pixel is designated as a center pixel in a region of adjacent pixels, an average value for the pixels in the region is determined, the average value is set as a threshold value, the value of each pixel in the region is compared to the threshold, each pixel in the region is assigned a binary value based on the comparison, and a binary number is generated for the region. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system configured to perform image analysis for detecting electronic device use by a driver of a vehicle comprising:
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a non-multispectral image capture device operably connected to a data processing device that captures a monochrome NIR image of a target vehicle; and a processor-usable medium embodying computer code, said processor-usable medium being coupled to said data processing device, said computer program code comprising instructions executable by sad processor and configured for; locating a windshield region of the vehicle in the captured image;
processing pixels of the windshield region of the image for computing a feature vector describing the windshield region of the vehicle, the feature vector defining a set of generic features that describe the entire windshield region;applying the feature vector to a classifier for classifying the image into respective classes including at least classes for candidate electronic device use and candidate electronic device non-use; and
, outputting the classification;wherein processing the pixels of the windshield region includes generating a global descriptor describing the entire image using a process selected from a group consisting of; a Successive Mean Quantization Transform (SMQT); a Scale-Invariant Feature Transform (SIFT); a Histogram of Gradients (HOG); a Bag-of-Visual-Words Representation; a Fisher Vector (FV) Representation; and
,a combination of the above; wherein the SMQT process includes determining a feature vector for each pixel in the image by analyzing adjacent pixels in the region of the pixel, wherein for each pixel, the pixel is designated as a center pixel in a region of adjacent pixels, an average value for the pixels in the region is determined, the average value is set as a threshold value, the value of each pixel in the region is compared to the threshold, each pixel in the region is assigned a binary value based on the comparison, and a binary number is generated for the region. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-usable medium for performing image analysis for detecting electronic device use by a driver of a vehicle said computer-usable medium embodying a computer program code, said computer program code comprising computer executable instructions configured for:
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acquiring a monochrome NIR image including a vehicle from an associated image capture device positioned to view oncoming traffic; locating a windshield region of the vehicle in the captured image;
processing pixels of the windshield region of the image for computing a feature vector describing the windshield region of the vehicle, the feature vector defining a set of generic features that describe the entire windshield region without searching for specific image content within the windshield region;applying the feature vector to a classifier for classifying the image into respective classes including at least classes for candidate electronic device use and candidate electronic device non-use; and
,outputting the classification; wherein processing the pixels of the windshield region includes generating a global descriptor describing the entire image using a process selected from a group consisting of; a Successive Mean Quantization Transform (SMQT); a Scale-Invariant Feature Transform (SIFT); a Histogram of Gradients (HOG); a Bag-of-Visual-Words Representation; a Fisher Vector (FV) Representation; and
,a combination of the above; wherein the SMQT process includes determining a feature vector for each pixel in the image by analyzing adjacent pixels in the pixel, wherein for each pixel, the pixel is designated as a center region of adjacent pixels, an average value for the pixels in the region is determined, the average value is set as a threshold value, the value of each pixel in the region is compared to the threshold, each pixel in the region is assigned a binary value based on the comparison, and a binary number is generated for the region. - View Dependent Claims (16, 17)
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Specification