Abstract: A computer vision system for automatic identification, tracking and management of inventory and/or assets, wherein: the computer vision system is programmed with predetermined and configurable image data conditions that identify and capture certain types of movement; the computer vision system is programmed to constantly capture and process image data from one or more sets of sensors to detect the image data conditions; the computer vision system is programmed to trigger the process of identifying all objects and extracting label information from objects present in a field of view of the system if the image data conditions are fulfilled; the computer vision system processes the image data with one or more object detection algorithms and label detection algorithms and generate correspondences between objects and labels identified; and the computer vision system running change detection algorithms and semantic analysis to detect if any object was moved, removed, occluded and if any new objects were added in to

Abstract: A system and method of identifying and tracking objects comprises registering an identity of a person who visits an area designated for holding objects, capturing an image of the area designated for holding objects, submitting a version of the image to a deep neural network trained to detect and recognize objects in images like those objects held in the designated area, detecting an object in the version of the image, associating the registered identity of the person with the detected object, retraining the deep neural network using the version of the image if the deep neural network is unable to recognize the detected object, and tracking a location of the detected object while the detected object is in the area designated for holding objects.

Abstract: A computer vision system for automatic identification, tracking and management of inventory and/or assets, wherein: the computer vision system is programmed with predetermined and configurable image data conditions that identify and capture certain types of movement; the computer vision system is programmed to constantly capture and process image data from one or more sets of sensors to detect the image data conditions; the computer vision system is programmed to trigger the process of identifying all objects and extracting label information from objects present in a field of view of the system if the image data conditions are fulfilled; the computer vision system processes the image data with one or more object detection algorithms and label detection algorithms and generate correspondences between objects and labels identified; and the computer vision system running change detection algorithms and semantic analysis to detect if any object was moved, removed, occluded and if any new objects were added in to

Abstract: A system and method of identifying and tracking objects comprises registering an identity of a person who visits an area designated for holding objects, capturing an image of the area designated for holding objects, submitting a version of the image to a deep neural network trained to detect and recognize objects in images like those objects held in the designated area, detecting an object in the version of the image, associating the registered identity of the person with the detected object, retraining the deep neural network using the version of the image if the deep neural network is unable to recognize the detected object, and tracking a location of the detected object while the detected object is in the area designated for holding objects.

Abstract: A system and method of identifying and tracking objects comprises registering an identity of a person who visits an area designated for holding objects, capturing an image of the area designated for holding objects, submitting a version of the image to a deep neural network trained to detect and recognize objects in images like those objects held in the designated area, detecting an object in the version of the image, associating the registered identity of the person with the detected object, retraining the deep neural network using the version of the image if the deep neural network is unable to recognize the detected object, and tracking a location of the detected object while the detected object is in the area designated for holding objects.

Abstract: A system and method of calibrating a light source relative to an optical device comprise virtually projecting by an image projector an image onto a three-dimensional surface of at least one object, the image comprising a virtual grid comprised of predefined points onto an area having the three-dimensional surface of at least one object, each predefined point of the virtual grid corresponding to one of a plurality of termination points of beams cast by the directional light source, at least one of the termination points of the beams are on a surface of the at least one object; simultaneously capturing by at least one optical device data for a plurality of points on the virtual grid by projecting the image into a field of view of the at least one optical device; and calibrating a location and orientation of the light source relative to the optical device using the captured data for the plurality of points.

Abstract: A computer vision system for automatic identification, tracking and management of inventory and/or assets, wherein: the computer vision system is programmed with predetermined and configurable image data conditions that identify and capture certain types of movement; the computer vision system is programmed to constantly capture and process image data from one or more sets of sensors to detect the image data conditions; the computer vision system is programmed to trigger the process of identifying all objects and extracting label information from objects present in a field of view of the system if the image data conditions are fulfilled; the computer vision system processes the image data with one or more object detection algorithms and label detection algorithms and generate correspondences between objects and labels identified; and the computer vision system running change detection algorithms and semantic analysis to detect if any object was moved, removed, occluded and if any new objects were added in to

Abstract: A system and method for calibrating location and orientation of a directional light source relative to a field of view of an optical device includes a directional light source that directs light to points on a virtual grid overlaying the field of view of the optical device. The optical device captures an image for each point on the virtual grid at which the directional light source directs light. A light dot is located in a plurality of captured images. The location and orientation of the directional light source are calibrated relative to the field of view of the optical device based on coordinates of each located light dot and on relative coordinates of the optical device.

Abstract: Position tracking systems and methods for tracking a physical location of a radio frequency (RF) transmitter include an RF transmitter transmitting an RF signal from a plurality of known locations. At least three RF receiver antennae are disposed at undetermined locations within range of the RF transmitter to receive the RF signals transmitted from the plurality of known locations. A receiver station in communication with the at least three RF receiver antennae initially calibrates a relative position of each RF receiver antenna with respect to the other RF receiver antennae based on the plurality of known locations and on information acquired in response to the RF signals received at the at least three RF receiver antennae.

Abstract: Position and orientation tracking systems and methods include a transmitting antenna transmitting a radio frequency (RF) signal. At least one receiving antenna acquires the RF signal. One of the at least one receiving antenna and the transmitting antenna is designated a reference antenna. A processing unit determines a phase difference between the RF signal received by each receiving antenna and the reference antenna. The processing unit computes a position of the transmitting antenna with respect to the at least one receiving antenna in response to the phase difference determined for each receiving antenna.

Abstract: A system and method of identifying and tracking objects comprises registering an identity of a person who visits an area designated for holding objects, capturing an image of the area designated for holding objects, submitting a version of the image to a deep neural network trained to detect and recognize objects in images like those objects held in the designated area, detecting an object in the version of the image, associating the registered identity of the person with the detected object, retraining the deep neural network using the version of the image if the deep neural network is unable to recognize the detected object, and tracking a location of the detected object while the detected object is in the area designated for holding objects.

Abstract: Systems and methods for position tracking include at least three spatially separated radio-frequency (RF) sources of radio signals. Each RF source is disposed in an area at a location known to that RF source. Each RF source includes an RF transmitter configured to transmit radio signals that carry a unique identifier and the known location of that RF source. The position tracking system and method include at least one tag. Each tag is coupled to a different mobile object and includes a radio module with an RF receiver for receiving the radio signals transmitted by the at least three spatially separated RF sources. Each tag further includes memory and a processor that executes executable code stored in the memory to compute a range from that tag to each of the at least three RF sources and to compute a position of that tag based on at least three computed ranges.

Abstract: A system and method for calibrating location and orientation of a directional light source relative to a field of view of an optical device includes a directional light source that directs light to points on a virtual grid overlaying the field of view of the optical device. The optical device captures an image for each point on the virtual grid at which the directional light source directs light. A light dot is located in a plurality of captured images. The location and orientation of the directional light source are calibrated relative to the field of view of the optical device based on coordinates of each located light dot and on relative coordinates of the optical device.

Abstract: Position tracking systems and methods for tracking a physical location of a radio frequency (RF) transmitter include an RF transmitter transmitting an RF signal from a plurality of known locations. At least three RF receiver antennae are disposed at undetermined locations within range of the RF transmitter to receive the RF signals transmitted from the plurality of known locations. A receiver station in communication with the at least three RF receiver antennae initially calibrates a relative position of each RF receiver antenna with respect to the other RF receiver antennae based on the plurality of known locations and on information acquired in response to the RF signals received at the at least three RF receiver antennae.

Abstract: Position tracking systems and methods for tracking a physical location of a radio frequency (RF) transmitter include an RF transmitter transmitting an RF signal from a plurality of known locations. At least four RF receiver antennae are disposed at unknown locations within range of the RF transmitter to receive the RF signals transmitted from the plurality of known locations. A receiver station in communication with the at least four RF receiver antennae initially calibrates a relative position of each RF receiver antenna with respect to the other RF receiver antennae based on the plurality of known locations and on information acquired in response to the RF signals received at the at least four RF receiver antennae.

Abstract: Position and orientation tracking systems and methods include a transmitting antenna transmitting a radio frequency (RF) signal. At least one receiving antenna acquires the RF signal. One of the at least one receiving antenna and the transmitting antenna is designated a reference antenna. A processing unit determines a phase difference between the RF signal received by each receiving antenna and the reference antenna. The processing unit computes a position of the transmitting antenna with respect to the at least one receiving antenna in response to the phase difference determined for each receiving antenna.

Abstract: Position and orientation tracking systems and methods include a transmitting antenna transmitting a radio frequency (RF) signal. At least one receiving antenna acquires the RF signal. One of the at least one receiving antenna and the transmitting antenna is designated a reference antenna. A processing unit determines a phase difference between the RF signal received by each receiving antenna and the reference antenna. The processing unit computes a position of the transmitting antenna with respect to the at least one receiving antenna in response to the phase difference determined for each receiving antenna.

Abstract: Tracking systems and methods for obtaining position coordinates of transmitters are provided. One or more transmitters send multiple carrier signals to multiple receivers, where the time difference of arrival of the multiple carrier signals are used to determine the location of each transmitter. Accuracy is obtained by using phase information of multiple carrier frequencies for time difference of arrival measurements. The accuracy obtained by a receiver depends on the quality of the received carrier signal; a received carrier signal may become distorted by the presence of multipath interference. By using multiple signals with different frequencies, the system can screen or compensate for multipath effects. This screening can be accomplished either through various signal-sampling techniques or by averaging the signals received at the receiver. Because signals with different frequencies have different multipath experiences, a computer can analyze and compensate for “good” and “bad” signals.

Abstract: Systems and methods for position tracking include at least three spatially separated radio-frequency (RF) sources of radio signals. Each RF source is disposed in an area at a location known to that RF source. Each RF source includes an RF transmitter configured to transmit radio signals that carry a unique identifier and the known location of that RF source. The position tracking system and method include at least one tag. Each tag is coupled to a different mobile object and includes a radio module with an RF receiver for receiving the radio signals transmitted by the at least three spatially separated RF sources. Each tag further includes memory and a processor that executes executable code stored in the memory to compute a range from that tag to each of the at least three RF sources and to compute a position of that tag based on at least three computed ranges.

Abstract: Position tracking systems and methods for tracking a physical location of a radio frequency (RF) transmitter include an RF transmitter transmitting an RF signal from a plurality of known locations. At least four RF receiver antennae are disposed at unknown locations within range of the RF transmitter to receive the RF signals transmitted from the plurality of known locations. A receiver station in communication with the at least four RF receiver antennae initially calibrates a relative position of each RF receiver antenna with respect to the other RF receiver antennae based on the plurality of known locations and on information acquired in response to the RF signals received at the at least four RF receiver antennae.