Abstract: Detection models are distributed as needed to a select subset of image capture devices, based on geographic location and time of the image capture devices. Each image capture device which receives a model processes image data according to the model, and provides a notification of any detected instances of an event the model is trained to detect. Distribution and detection can be based on historical data or live data.

Abstract: Detection models are distributed as needed to a select subset of image capture devices, based on geographic location and time of the image capture devices. Each image capture device which receives a model processes image data according to the model, and provides a notification of any detected instances of an event the model is trained to detect. Distribution and detection can be based on historical data or live data.

Abstract: Systems and methods by a telematics server are provided. The method includes receiving, over a network, training data including model input data and a known output label corresponding to the model input data from a first device, training a centralized machine-learning model using the training data, determining, by the centralized machine-learning model, an output label prediction certainty based on the model input data, determining an increase in the output label prediction certainty over a prior predicted output label certainty of the centralized machine-learning model, and sending, over the network, a machine-learning model update to a second device in response to determining that the increase in the output label prediction certainty is greater than an output label prediction increase threshold.

Abstract: Methods for vehicle data collection by image analysis are provided. An example method involves positioning a camera in a vehicle to be pointed toward a field of interest in the vehicle, capturing an image of the field of interest with the camera, identifying a region of interest in the image that is expected to convey vehicle information, and running an image processing model over the region of interest to extract vehicle information from the image.

Abstract: Systems and methods for training image processing models for vehicle data collection by image analysis are provided. An example method involves accessing an image of a field of interest in a vehicle captured by a camera in the vehicle, providing a user interface to, display the image, receive input that defines a region of interest in the image that is expected to convey vehicle information, and receive input that assigns a label to the region of interest that associates the region of interest with an image processing model that is to be trained to extract a type of vehicle information from the region of interest, and contributing the image, labelled with the region of interest and the label associating the region of interest to the image processing model, to a training data library to train the image processing model.

Abstract: Systems and methods by a telematics server are provided. The method includes receiving, over a network, training data including model input data and a known output label corresponding to the model input data from a first device, training a centralized machine-learning model using the training data, determining, by the centralized machine-learning model, an output label prediction certainty based on the model input data, determining an increase in the output label prediction certainty over a prior predicted output label certainty of the centralized machine-learning model, and sending, over the network, a machine-learning model update to a second device in response to determining that the increase in the output label prediction certainty is greater than an output label prediction increase threshold.

Abstract: An input/output (I/O) expansion adapter and a method by an I/O expansion adapter are provided. The method includes receiving raw I/O expansion data from an I/O expander coupled thereto, processing the raw I/O expansion into processed I/O expansion data, and sending the processed I/O expansion data to the telematics device.

Abstract: Detection models are distributed as needed to a select subset of image capture devices, based on geographic location and time of the image capture devices. Each image capture device which receives a model processes image data according to the model, and provides a notification of any detected instances of an event the model is trained to detect. Distribution and detection can be based on historical data or live data.

Abstract: Detection models are distributed as needed to a select subset of image capture devices, based on geographic location and time of the image capture devices. Each image capture device which receives a model processes image data according to the model, and provides a notification of any detected instances of an event the model is trained to detect. Distribution and detection can be based on historical data or live data.

Abstract: Methods for vehicle data collection by image analysis are provided. An example method involves positioning a camera in a vehicle to be pointed toward a field of interest in the vehicle, capturing an image of the field of interest with the camera, identifying a region of interest in the image that is expected to convey vehicle information, and running an image processing model over the region of interest to extract vehicle information from the image.

Abstract: Systems and methods for training image processing models for vehicle data collection by image analysis are provided. An example method involves accessing an image of a field of interest in a vehicle captured by a camera in the vehicle, providing a user interface to, display the image, receive input that defines a region of interest in the image that is expected to convey vehicle information, and receive input that assigns a label to the region of interest that associates the region of interest with an image processing model that is to be trained to extract a type of vehicle information from the region of interest, and contributing the image, labelled with the region of interest and the label associating the region of interest to the image processing model, to a training data library to train the image processing model.

Abstract: Systems and methods for vehicle data collection by image analysis are provided. An example method involves positioning a camera in a vehicle to be pointed toward a field of interest in the vehicle, capturing an image of the field of interest with the camera, identifying a region of interest in the image that is expected to convey vehicle information, and running an image processing model over the region of interest to extract vehicle information from the image.

Abstract: An apparatus is provided for detecting a shopping cart used to bring items to a checkout location, and inspecting its bottom tray for the presence of items. The apparatus includes a cart-detector to detect the presence of the cart at a pre-determined location; and an item-detector to detect the presence of items placed on the lower tray of the cart. The cart detector includes an arrangement of three optical sensors and corresponding retro-reflective targets; and finite state-machine processing logic to discriminate a specific sequence of sensor responses.

Abstract: An apparatus is provided for detecting a shopping cart used to bring items to a checkout location, and inspecting its bottom tray for the presence of items. The apparatus includes a cart-detector to detect the presence of the cart at a predetermined location; and an item-detector to detect the presence of items placed on the lower tray of the cart. The cart detector includes an arrangement of three optical sensors and corresponding retro-reflective targets; and finite state-machine processing logic to discriminate a specific sequence of sensor responses.

Abstract: An extendible wrist mechanism comprises a base and a spaced end plate to which an end effector or other tool holder may be mounted. Three linear actuators are disposed about the central axis directed from the base to the end plate. Each actuator is joined to the base by a joint permitting pivoting toward and away from the central axis and pivoting tangentially of a circle centered about the central axis. Each linear actuator is mounted to the end plate by a joint permitting pitch, yaw, and roll of the end member with respect to the linear actuator. A tube extends from the base and telescopingly receives a member which is attached to the end plate at the central axis by a U-joint permitting pitch and yaw of the end plate. The base mounted tube is keyed to the end plate mounted member so that the end plate is held against rotation with respect to the base.