Occupancy detection for managed lane enforcement based on localization and classification of windshield images
View Patent ↗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.
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 descriptor describing a cabin region of the vehicle;
determining a distance that the feature descriptor is positioned in a projected feature space relative to a hyper-plane;
in response to the distance meeting and exceeding a predetermined threshold, applying the feature descriptor 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.
2. The method of claim 1 further comprising:
locating a windshield of the vehicle in the captured image; and,
extracting the windshield region from the image and eliminating other regions.
3. The method of claim 1 further comprising:
locating a windshield of the vehicle in the captured image;
defining a passenger side and a driver side within the windshield region; and,
extracting the passenger side of the windshield region from the image and eliminating the driver side.
4. The method of claim 1 , wherein the processing the image is performed by 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.
5. The method of claim 1 further comprising:
before acquiring the image, training a classifier using at least a first sample image including a driver and a passenger occupying a vehicle and at least a second sample image including a driver and no passenger occupying the vehicle.
6. The method of claim 1 further comprising:
in response to the vehicle being classified as a candidate violator, performing further processing on the image for determining whether the vehicle is one of a violator and non-violator.
7. The method of claim 1 wherein the outputting further includes:
providing notice of a candidate violator to an associated user device.
8. A computer program product comprising non-transitory computer-readable recording media which encodes instructions for performing the method of claim 1 .
9. A system for detecting a vehicle occupancy violation, the system comprising:
a determination device comprising memory which stores instructions for performing the method of claim 1 and a processor, in communication with the memory for executing the instructions.
10. A system for detecting a vehicle occupancy violation, the system comprising:
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 descriptor describing a cabin region of the vehicle,
a classifier adapted to:
determine a distance that the feature descriptor is positioned in a projected feature space relative to a hyper-plane;
in response to the distance meeting and exceeding a predetermined threshold, use the feature descriptor 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.
11. The system according to claim 10 further comprising an image capture device having near-infrared capability.
12. The system according to claim 10 , wherein the feature extraction module is adapted to perform 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.
13. The system according to claim 10 , wherein the feature extraction module is adapted to:
divide the image into sub-images;
process each sub-image to determine low-level features representing each sub-image; and,
concatenate the low-level features into a feature vector.
14. The system according to claim 10 further comprising a user device adapted to receive as notice the classification indicating that that the vehicle is a candidate violator.
15. The system according to claim 10 , wherein the violation determination device is further adapted to:
in response to the vehicle being classified as belonging to a candidate violator, perform further processing on the image for determining whether the vehicle is one of a violator and non-violator.
16. The system according to claim 10 , wherein the outputting further includes:
providing notice of a candidate violator to an associated user device.
17. The system according to claim 10 further comprising:
a windshield localization module adapted to extract the windshield region from the image.
18. The system according to claim 10 , wherein the classifier is adapted to:
acquire a set of sample images including at least a first image of a vehicle with a passenger and a second image of a vehicle with no passenger;
process the sample images for descriptors;
associate each of the first and second images with the classifications; and,
transmit the associations to the storage device for storing the classifications.
19. A system for detecting a vehicle occupancy violation, the system comprising:
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 feature 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:
determine a distance that the feature descriptor is positioned in a projected feature space relative to a hyper-plane;
apply the feature descriptor to a distance based thresholding parameter; and
in response to the distance meeting and exceeding the distance based thresholding parameter, selectively classify the image;
a processor adapted to implement the modules; and,
a graphic user interface adapted to output the classification.