Occupancy detection for managed lane enforcement based on localization and classification of windshield images
First Claim
1. A method for detecting a vehicle occupancy violation, the method comprising:
- acquiring an image including a vehicle from an associated image capture device positioned to view oncoming traffic;
processing pixels of the image for computing a feature vector describing a cabin region of the vehicle;
applying the feature vector to a classifier for classifying the image into respective classes including at least classes for a candidate violator and a non-violator; and
,outputting the classification.
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Abstract
A system for detecting a vehicle occupancy violation includes an image capture module that acquires an image including a vehicle cabin from a camera positioned to view oncoming traffic. The system includes a violation determination device, which includes a feature extraction module that processes the image pixels for determining an image descriptor. The process is selected from a group consisting of a Successive Mean Quantization Transform; a Scale-Invariant Feature Transform; a Histogram of Gradients; a Bag-of-Visual-Words Representation; a Fisher Vector Representation; and, a combination of the above. The system further includes a classifier that determines a distance that the vehicle image descriptor/representation is positioned in the projected feature space relative to a hyper-plane. The classifier determines whether the distance meets a threshold and classifies the image when the threshold is met. A processor implements the modules. A graphic user interface outputs the classification.
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Citations
20 Claims
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1. A method for detecting a vehicle occupancy violation, the method comprising:
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acquiring an image including a vehicle from an associated image capture device positioned to view oncoming traffic; processing pixels of the image for computing a feature vector describing a cabin region of the vehicle; applying the feature vector to a classifier for classifying the image into respective classes including at least classes for a candidate violator and a non-violator; and
,outputting the classification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for detecting a vehicle occupancy violation, the system comprising:
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an image capture module adapted to acquire an image including a cabin of a vehicle from an associated image capture device positioned to view oncoming traffic; a violation determination device adapted for processing the image, the device including; a feature extraction module adapted to compute from pixels of the image a feature vector describing a cabin region of the vehicle, a classifier adapted to use the feature vector for classifying the image into respective classes including at least classes for a candidate violator and a non-violator, a processor adapted to implement the modules; a storage device adapted to store classifications associated with corresponding descriptors; and
,a graphic user interface adapted to output the classification. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A system for detecting a vehicle occupancy violation, the system comprising:
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an image capture module adapted to acquire an image including a cabin of a vehicle from an associated image capture device positioned to view oncoming traffic; a violation determination device adapted for processing the image, the device including; a feature extraction module adapted to; process pixels of the image for determining a descriptor of the image, wherein the process is selected from a group consisting;
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.a classifier adapted to; separate representations corresponding to violating and non-violating images, compute a score that reflects a likelihood that an image corresponds to a violating vehicle, determine whether the score meets a threshold, and classify the image in response to the score meeting and exceeding the threshold; a processor adapted to implement the modules; and
,a graphic user interface adapted to output the classification.
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Specification