The present invention is a method and system to verify carpool occupancy compliance for access to High Occupancy Vehicle (HOV) lanes, High Occupancy or Toll (HOT) lanes, or other vehicle-occupancy contingent rewards. The present invention uses software and hardware devices with radio-frequency transmitter modules to capture one or more photo images of vehicle occupants and to perform boxed headcounts of humans in any given photo frame. The present invention uses biometric signature detection to confirm the boxed headcounts and a realness algorithm to further confirm the genuineness of any human image. Occupancy compliance can be communicated directly to an appropriate regulatory body.

An apparatus includes a camera and a processor circuit. The camera may be configured to capture color images in response to visible light and monochrome infrared images in response to infrared light. The processor circuit may be configured to extract color features from the color images and add color to corresponding monochrome features detected in the monochrome infrared images.

Devices, systems, and methods for remote control of autonomous vehicles are disclosed herein. A method may include receiving, by a device, first data indicative of an autonomous vehicle in a parking area, and determining, based on the first data, a location of the autonomous vehicle. The method may include determining, based on a the location, first image data including a representation of an object. The method may include generating second image data based on the first data and the first image data, and presenting the second image data. The method may include receiving an input associated with controlling operation of the autonomous vehicle, and controlling, based on the input, the operation of the autonomous vehicle.

A system for identifying desired information from collected sensor data includes a collection device and a processing module. The collection device collects sensor data, coarsely filters the sensor data according to predefined rules to generate filter matched data, and securely transmits the filter matched data to the processing module. The processing module finely filters the filter matched data to generate desired information, provides the desired information to an authorized actor, and deletes the filter matched data.

A system includes a processor and a memory. The memory stores instructions executable by the processor to receive an image from a stationary camera. The memory stores instructions to determine location coordinates of an object identified in the image based on location coordinates specified for the image. The memory stores instructions to operate a vehicle based on the object location coordinates.

A system includes a processor and a memory. The memory stores instructions executable by the processor to receive an image from a stationary camera. The memory stores instructions to determine location coordinates of an object identified in the image based on location coordinates specified for the image. The memory stores instructions to operate a vehicle based on the object location coordinates.

To provide a learning device that improves, using appropriate learning data, the accuracy of a prediction model that predicts a traffic event from a video. The learning device: detects, from a video obtained by imaging a road, an object to be detected including at least a vehicle, by a method different from that of a prediction model that predicts a traffic event on the road; generates learning data for the prediction model on the basis of the detected object and the captured video; and learns the prediction model using the generated learning data.

To provide a learning device that improves, using appropriate learning data, the accuracy of a prediction model that predicts a traffic event from a video. The learning device: detects, from a video obtained by imaging a road, an object to be detected including at least a vehicle, by a method different from that of a prediction model that predicts a traffic event on the road; generates learning data for the prediction model on the basis of the detected object and the captured video; and learns the prediction model using the generated learning data.

An image identification method is applied to an image surveillance apparatus and includes utilizing an object detecting function to generate a first bounding box and a second bounding box within a surveillance image, utilizing a foreground detecting function to generate a plurality of markers within the surveillance image, defining some of the plurality of markers which conform to the first bounding box as a first marker group and further defining other markers which do not conform to the first bounding box as a second marker group, determining whether the second marker group conforms to the second bounding box, and deciding whether the second bounding box belongs to an error of the object detecting function in accordance with a determination result.

An image identification method is applied to an image surveillance apparatus and includes utilizing an object detecting function to generate a first bounding box and a second bounding box within a surveillance image, utilizing a foreground detecting function to generate a plurality of markers within the surveillance image, defining some of the plurality of markers which conform to the first bounding box as a first marker group and further defining other markers which do not conform to the first bounding box as a second marker group, determining whether the second marker group conforms to the second bounding box, and deciding whether the second bounding box belongs to an error of the object detecting function in accordance with a determination result.