Abstract: Technology disclosed herein provides an object tracking multi-camera system including a plurality of cameras to capture images of one or more objects and one or more pattern recognition modules, each of the pattern recognition modules configured to identify an object in a video frame using pattern recognition and assigning a tracking number to the identified object, collect a plurality of object frames related to the identified object and associating the plurality of object frames to the assigned tracking number, compare a newly obtained object frame with the plurality of object frames related to the identified object to determine if the newly obtained object frame is related to the identified object, and in response to the comparison determine that facial recognition does not need to be performed on the newly obtained object frame.

Abstract: Technology disclosed herein provides an object tracking multi-camera system including a plurality of cameras to capture images of one or more objects and one or more pattern recognition modules, each of the pattern recognition modules configured to identify an object in a video frame using pattern recognition and assigning a tracking number to the identified object, collect a plurality of object frames related to the identified object and associating the plurality of object frames to the assigned tracking number, compare a newly obtained object frame with the plurality of object frames related to the identified object to determine if the newly obtained object frame is related to the identified object, and in response to the comparison determine that facial recognition does not need to be performed on the newly obtained object frame.

Abstract: Systems and methods are disclosed for distributed decentralized machine learning model training. In certain embodiments, a first node in a network may comprise a circuit configured to receive an initial machine learning model having an initial parameter set, apply the local data to update parameters of the initial machine learning model to generate an updated machine learning model, transmit a copy of the updated machine learning model from the first node to a plurality of neighboring nodes in the network via a network interface, receive, via the network interface, a modified machine learning model from a first neighboring node, the modified machine learning model having parameters set based on local data of the first neighboring node, modify the updated machine learning model based on the modified machine learning model, and apply the updated machine learning model to control operations at the first node.