Abstract: Measuring speed of a vehicle in a road environment. During calibration, multiple images are captured of a calibration vehicle traveling at a known ground speed. A calibration image feature is located in the image of the calibration vehicle. An optical flow of the calibration image feature is computed to determine a model between an image speed of the calibration image feature and the known ground speed of the calibration vehicle. During speed measurement, multiple images are captured of a target vehicle traveling along a road surface at unknown ground speed. A target image feature may be located in an image of the target vehicle. An image speed may be computed of the target image feature. The model may be applied to determine the ground speed of the target vehicle from the image speed of the target image feature.

Abstract: Measuring speed of a vehicle in a road environment. During calibration, multiple images are captured of a calibration vehicle traveling at a known ground speed. A calibration image feature is located in the image of the calibration vehicle. An optical flow of the calibration image feature is computed to determine a model between an image speed of the calibration image feature and the known ground speed of the calibration vehicle. During speed measurement, multiple images are captured of a target vehicle traveling along a road surface at unknown ground speed. A target image feature may be located in an image of the target vehicle. An image speed may be computed of the target image feature. The model may be applied to determine the ground speed of the target vehicle from the image speed of the target image feature.

Abstract: Determining that a motor vehicle driver is using a mobile device while driving a motor vehicle. Multiple images of a driver of a motor vehicle are captured through a side window of the motor vehicle. Positive images show a driver using a mobile device while driving a motor vehicle. Negative images show a driver not using a mobile device while driving a motor vehicle. Multiple training images are selected from both the positive images and the negative images. The selected training images and respective labels, indicating that the selected training images are positive images or negative images, are input to a machine (e.g. Convolutional Neural Network, (CNN)). The CNN is trained to classify that a test image, captured through a side window of a motor vehicle, shows a driver using a mobile device while driving the motor vehicle.

Abstract: Measuring speed of a vehicle in a road environment. During calibration, multiple images are captured of a calibration vehicle traveling at a known ground speed. A calibration image feature is located in the image of the calibration vehicle. An optical flow of the calibration image feature is computed to determine a model between an image speed of the calibration image feature and the known ground speed of the calibration vehicle. During speed measurement, multiple images are captured of a target vehicle traveling along a road surface at unknown ground speed. A target image feature may be located in an image of the target vehicle. An image speed may be computed of the target image feature. The model may be applied to determine the ground speed of the target vehicle from the image speed of the target image feature.

Abstract: Determining that a motor vehicle driver is using a mobile device while driving a motor vehicle. Multiple images of a driver of a motor vehicle are captured through a side window of the motor vehicle. Positive images show a driver using a mobile device while driving a motor vehicle. Negative images show a driver not using a mobile device while driving a motor vehicle. Multiple training images are selected from both the positive images and the negative images. The selected training images and respective labels, indicating that the selected training images are positive images or negative images, are input to a machine (e.g. Convolutional Neural Network, (CNN)). The CNN is trained to classify that a test image, captured through a side window of a motor vehicle, shows a driver using a mobile device while driving the motor vehicle.

Abstract: Determining that a motor vehicle driver is using a mobile device while driving a motor vehicle. Multiple images of a driver of a motor vehicle are captured through a side window of the motor vehicle. Positive images show a driver using a mobile device while driving a motor vehicle. Negative images show a driver not using a mobile device while driving a motor vehicle. Multiple training images are selected from both the positive images and the negative images. The selected training images and respective labels, indicating that the selected training images are positive images or negative images, are input to a machine (e.g. Convolutional Neural Network, (CNN)). The CNN is trained to classify that a test image, captured through a side window of a motor vehicle, shows a driver using a mobile device while driving the motor vehicle.

Abstract: Measuring speed of a vehicle in a road environment. During calibration, multiple images are captured of a calibration vehicle traveling at a known ground speed. A calibration image feature is located in the image of the calibration vehicle. An optical flow of the calibration image feature is computed to determine a model between an image speed of the calibration image feature and the known ground speed of the calibration vehicle. During speed measurement, multiple images are captured of a target vehicle traveling along a road surface at unknown ground speed. A target image feature may be located in an image of the target vehicle. An image speed may be computed of the target image feature. The model may be applied to determine the ground speed of the target vehicle from the image speed of the target image feature.

Abstract: Determining that a vehicle is speeding in violation of a speeding regulation. Capture a timed sequence of image frames of the target vehicle at known capture times of the vehicle, identify corresponding images of a license plate of the target vehicle in the image frames, recognize an alphanumeric character of the license plate in the corresponding images of the license plate, measure respective image dimensions of the alphanumeric character; and responsive to a change between the image dimensions of the alphanumeric character, compute a speed of the target vehicle relative to the camera. Font style and font size are stored of one or more standard vehicle license plate.

Abstract: Determining that a motor vehicle driver is using a mobile device while driving a motor vehicle. Multiple images of a driver of a motor vehicle are captured through a side window of the motor vehicle. Positive images show a driver using a mobile device while driving a motor vehicle. Negative images show a driver not using a mobile device while driving a motor vehicle. Multiple training images are selected from both the positive images and the negative images. The selected training images and respective labels, indicating that the selected training images are positive images or negative images, are input to a machine (e.g. Convolutional Neural Network, (CNN)). The CNN is trained to classify that a test image, captured through a side window of a motor vehicle, shows a driver using a mobile device while driving the motor vehicle.

Abstract: Determining that a vehicle is speeding in violation of a speeding regulation. Capture a timed sequence of image frames of the target vehicle at known capture times of the vehicle, identify corresponding images of a license plate of the target vehicle in the image frames, recognize an alphanumeric character of the license plate in the corresponding images of the license plate, measure respective image dimensions of the alphanumeric character; and responsive to a change between the image dimensions of the alphanumeric character, compute a speed of the target vehicle relative to the camera. Font style and font size are stored of one or more standard vehicle license plate.