Abstract: A simulation platform may receive, from a plurality of image capture devices, a plurality of image streams that depict an event. The simulation platform may identify an object that is depicted in each of the plurality of image streams. The simulation platform may determine, for each of the plurality of image streams, respective image-based coordinates of a path associated with the object during the event. The simulation platform may determine, based on the respective image-based coordinates and timestamps of the plurality of image streams, simulation coordinates associated with a path of the object during the event. The simulation platform may detect, based on the simulation coordinates, that the object is involved in a collision during the event. The simulation platform may perform an action associated with detecting that the object is involved in the collision.

Abstract: A smart conveyor belt web service is provided. A network device generates recipes for multiple orders. Each recipe provides sorting instructions for a conveyor system. The network device transfers an identifier for a first recipe to a queue of active recipes and sends the first recipe to a server device for the conveyor system. The network device receives a recipe-complete status message indicating completion of the first recipe. The network device deletes the indicator for the first recipe from the queue based on receiving the recipe-complete status message and transfers a second recipe to the queue of active recipes to initiate processing of another order.

Abstract: A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

Abstract: A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

Abstract: A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

Abstract: A simulation platform may receive, from a plurality of image capture devices, a plurality of image streams that depict an event. The simulation platform may identify an object that is depicted in each of the plurality of image streams. The simulation platform may determine, for each of the plurality of image streams, respective image-based coordinates of a path associated with the object during the event. The simulation platform may determine, based on the respective image-based coordinates and timestamps of the plurality of image streams, simulation coordinates associated with a path of the object during the event. The simulation platform may detect, based on the simulation coordinates, that the object is involved in a collision during the event. The simulation platform may perform an action associated with detecting that the object is involved in the collision.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll.

Abstract: A device may receive initial registration data identifying initial camera parameters associated with a camera device provided at a location, and may receive location data associated with the location captured by the camera device. The device may receive map data identifying a map image of the location, and may transform the initial registration data into estimated camera parameters. The device may process the location data, with the estimator model, to generate extracted data, and may process the estimated camera parameters, the extracted data, and the map data, with a parameter optimization model, to identify camera parameters for the camera device. The device may provide the camera parameters to the camera device to cause the camera device to be configured based on the camera parameters.

Abstract: A simulation platform may receive, from a plurality of image capture devices, a plurality of image streams that depict an event. The simulation platform may identify an object that is depicted in each of the plurality of image streams. The simulation platform may determine, for each of the plurality of image streams, respective image-based coordinates of a path associated with the object during the event. The simulation platform may determine, based on the respective image-based coordinates and timestamps of the plurality of image streams, simulation coordinates associated with a path of the object during the event. The simulation platform may detect, based on the simulation coordinates, that the object is involved in a collision during the event. The simulation platform may perform an action associated with detecting that the object is involved in the collision.

Abstract: A simulation platform may receive, from a plurality of image capture devices, a plurality of image streams that depict an event. The simulation platform may identify an object that is depicted in each of the plurality of image streams. The simulation platform may determine, for each of the plurality of image streams, respective image-based coordinates of a path associated with the object during the event. The simulation platform may determine, based on the respective image-based coordinates and timestamps of the plurality of image streams, simulation coordinates associated with a path of the object during the event. The simulation platform may detect, based on the simulation coordinates, that the object is involved in a collision during the event. The simulation platform may perform an action associated with detecting that the object is involved in the collision.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll.

Abstract: A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

Abstract: A device can receive data related to a roadway. The data can include video data of the roadway. The device can determine a pixel area of objects shown in the video data. The device can determine that a vehicle and an individual are associated with the roadway at a same time. The device can determine a protective zone around the individual based on a first pixel area of the individual. The protective zone can be associated with detecting a potential collision between the vehicle and the individual. The device can determine a first location and movement of the vehicle relative to a second location and movement of the individual. The device can predict that a second pixel area of the vehicle is to intersect the protective zone of the individual. The device can detect the potential collision. The device can perform one or more actions.

Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based device placement and configuration service is provided. The machine learning-based device placement and configuration system uses regression models calculated from installation and performance data. The regression model includes data associated with sites at which video cameras were previously installed and tested for detection accuracy levels possibly associated with a service area. With these generated models, information about the physical space, and desired performance criteria, designers optimize camera number, camera placement, and geo-location camera parameters. The device placement and configuration service may find an optimal camera placement and geo-location camera parameter set which satisfy certain criteria. The geo-location parameters include position, height, heading, pitch, and roll.