Abstract: A system and method for tracking and annotating products in a store shelf is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor and a sensor capable of measuring distance, determining an area of interest in the shelf, determining a polygonal representation of at least one product present in the area of interest, and assigning a unique polygonal profile to at least one product, and/or storing the unique profile in a database. Such The unique polygonal profile may include a unique ID obtained from a group of attributes. The unique profile may include at least a unique position and a unique centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.

Abstract: A system and method for tracking and annotating products in a store shelf is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor and a sensor capable of measuring distance, determining an area of interest in the shelf, determining a polygonal representation of at least one product present in the area of interest, and assigning a unique polygonal profile to at least one product, and/or storing the unique profile in a database. Such The unique polygonal profile may include a unique ID obtained from a group of attributes. The unique profile may include at least a unique position and a unique centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.

Abstract: A system and method for tracking and annotating products in a store shelf is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining an area of interest in the shelf, determining a polygonal representation of at least one product present in the area of interest, and assigning a unique polygonal profile to at least one product, and/or storing the unique profile in a database. Such The unique polygonal profile may include a unique ID obtained from a group of attributes. The unique profile may include at least a unique position and a unique centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.

Abstract: A system and method for tracking and annotating products in a store shelf is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining an area of interest in the shelf, determining a polygonal representation of at least one product present in the area of interest, and assigning a unique polygonal profile to at least one product, and/or storing the unique profile in a database. Such The unique polygonal profile may include a unique ID obtained from a group of attributes. The unique profile may include at least a unique position and a unique centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.

Abstract: A system and method for tracking and annotating objects in a 3D model is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining a reference signal feed from the signal feed, determining a volumetric representation of at least one object not present in the reference signal feed, detecting at least one object, person or animal using pre-trained convolutional neural network models and assigning a unique volumetric profile to at least one object, and/or storing the unique volumetric profile in an object database. Such CNN can be custom-trained to detect specific objects, animals or persons. The signal feed may include at least one 3D area of interest. Also, the unique volumetric profile may include a unique volumetric id obtained from a group of attributes. The unique volumetric profile may include at least a unique volumetric position and a unique volumetric centroid.

Abstract: A system and method for tracking and annotating objects in a 3D model is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining a reference signal feed from the signal feed, determining a volumetric representation of at least one object not present in the reference signal feed, detecting at least one object, person or animal using pre-trained convolutional neural network models and assigning a unique volumetric profile to at least one object, and/or storing the unique volumetric profile in an object database. Such CNN can be custom-trained to detect specific objects, animals or persons. The signal feed may include at least one 3D area of interest. Also, the unique volumetric profile may include a unique volumetric id obtained from a group of attributes. The unique volumetric profile may include at least a unique volumetric position and a unique volumetric centroid.

Abstract: A system and method for tracking and annotating objects in a 3D model is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining a reference signal feed from the signal feed, determining a volumetric representation of at least one object not present in the reference signal feed, assigning a unique volumetric profile to at least one object, and/or storing the unique volumetric profile in an object database. The signal feed may include at least one 3D area of interest. Also, the unique volumetric profile may include a unique volumetric id obtained from a group of attributes. The unique volumetric profile may include at least a unique volumetric position and a unique volumetric centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.

Abstract: A system and method for tracking and annotating objects in a 3D model is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining a reference signal feed from the signal feed, determining a volumetric representation of at least one object not present in the reference signal feed, assigning a unique volumetric profile to at least one object, and/or storing the unique volumetric profile in an object database. The signal feed may include at least one 3D area of interest. Also, the unique volumetric profile may include a unique volumetric id obtained from a group of attributes. The unique volumetric profile may include at least a unique volumetric position and a unique volumetric centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.

Abstract: A system and method for tracking and annotating objects in a 3D model is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining a reference signal feed from the signal feed, determining a volumetric representation of at least one object not present in the reference signal feed, assigning a unique volumetric profile to at least one object, and/or storing the unique volumetric profile in an object database. The signal feed may include at least one 3D area of interest. Also, the unique volumetric profile may include a unique volumetric id obtained from a group of attributes. The unique volumetric profile may include at least a unique volumetric position and a unique volumetric centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.

Abstract: system and method for tracking and annotating objects in a 3D model is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining a reference signal feed from the signal feed, determining a volumetric representation of at least one object not present in the reference signal feed, assigning a unique volumetric profile to at least one object, and/or storing the unique volumetric profile in an object database. The signal feed may include at least one 3D area of interest. Also, the unique volumetric profile may include a unique volumetric id obtained from a group of attributes. The unique volumetric profile may include at least a unique volumetric position and a unique volumetric centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.