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: 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: A system mountable in a host vehicle, configured to operate while the host vehicle is moving in a road environment. The system includes a backward-looking camera with field-of-view behind the host vehicle, a side-looking camera with field-of-view to a side of the host vehicle, a forward-looking camera with field-of-view in front of the host vehicle. A processor is configured to control the cameras, to acquire and store in memory images acquired from the cameras. The processor is configured to detect a target vehicle in the images of a one camera and track a feature of the target vehicle in the images of the other cameras. A traffic violation is declared when a feature of the target vehicle is identified in images of the other cameras.

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: A method of assessing the speed of a vehicle including the steps of obtaining a first image of a vehicle; obtaining a second image of the vehicle captured at a known time after capture of the first image; selecting a reference point on the vehicle in the first image; and indicating a location in the second image corresponding to a predicted location of the selected vehicle reference point assuming that the vehicle travels at a particular speed between the times of capture of the first and second images.

Abstract: An image recording apparatus suitable for recording a traffic signal violation at an intersection controlled by a traffic light is described. The image recording apparatus includes a first camera focused on the intersection, and a second camera focused on a first lane of a street leading to the intersection. A first control circuit is coupled to the first camera causes the first camera to capture a first image at substantially an instant of time that the traffic light indicates a change of traffic control status. The apparatus also includes a detection circuit to detect when a portion of a vehicle crosses a trigger location in the first lane after the change in traffic control status. A second control circuit coupled to the second camera causes the second camera to capture a close-up image of the vehicle in the event that the detection circuit detects the portion of the vehicle at the trigger location after a change in traffic control status.

Abstract: An image recording apparatus suitable for recording a traffic signal violation at an intersection controlled by a traffic light is described. The image recording apparatus includes a first camera focused on the intersection, and a second camera focused on a first lane of a street leading to the intersection. A first control circuit is coupled to the first camera causes the first camera to capture a first image at substantially an instant of time that the traffic light indicates a change of traffic control status. The apparatus also includes a detection circuit to detect when a portion of a vehicle crosses a trigger location in the first lane after the change in traffic control status. A second control circuit coupled to the second camera causes the second camera to capture a close-up image of the vehicle in the event that the detection circuit detects the portion of the vehicle at the trigger location after a change in traffic control status.

Abstract: A system for monitoring and reporting incidences of traffic violations at a traffic location is disclosed. The system comprises one or more digital still cameras and one or more digital video cameras system deployed at a traffic location. The camera system is coupled to a data processing system, which comprises an image processor for compiling vehicle and scene images produced by the digital camera system, a verification process for verifying the validity of the vehicle images, an image processing system for identifying driver information from the vehicle images, and a notification process for transmitting potential violation information to one or more law enforcement agencies. The video camera system is configured to record footage both before and after the offense is detected. The video camera system includes a non-stop video capture buffer that records the preceding few seconds of violation. The buffer holds a number of seconds of video data in memory. When an offense is detected, a timer is started.

Abstract: A method of assessing the speed of a vehicle including the steps of obtaining a first image of a vehicle; obtaining a second image of the vehicle captured at a known time after capture of the first image; selecting a reference point on the vehicle in the first image; and indicating a location in the second image corresponding to a predicted location of the selected vehicle reference point assuming that the vehicle travels at a particular speed between the times of capture of the first and second images.

Abstract: An image recording apparatus is disclosed for recording traffic signal violations such as a traffic light signal changing to a red light. The apparatus includes a capturing unit for capturing a first image substantially at an instant of time that a control signal changes its status and a storing unit for storing the first image at least temporarily. The apparatus includes a detecting unit for detecting presence of a vehicle or object beyond a location at which the vehicle or object is required to stop in response to the change in status and a device responsive to the detecting unit for recording a first image in the event that the detecting unit detects presence of the vehicle or object beyond the location. The apparatus may include a deleting unit for deleting the first image in the event that the detecting unit does not detect presence of the vehicle or object beyond the location.

Abstract: A method of processing digital image information, including pixel intensity information, includes the steps of identifying a region of the image in which pixel intensities are similar to each other, but the median pixel intensity differs significantly from the median pixel intensity of other parts of the image; and modifying pixel intensities in the identified region so that the median for the region is closer to the median for the other parts of the image. A related method of processing digital image information involves modifying pixel intensities according to a non-linear algorithm, with the result that one or more regions of the image have their median pixel intensities adjusted to a level which is closer to the median pixel intensity for other parts of the image.