Abstract: An adaptive real-time detection and examination network that employs deep learning to detect and recognize objects in a stream of pixilated two-dimensional digital images. The network provides the images from an image source as pixilated image frames to a CNN having an input layer and output layer, where the CNN identifies and classifies the objects in the image. The network also provides metadata relating to the image source and its location, and provides the object classification data and the metadata to an RNN that identifies motion and relative velocity of the classified objects in the images. The network combines the object classification data from the CNN and the motion data from the RNN, and correlates the combined data to define boundary boxes around each of the classified objects and an indicator of relative velocity and direction of movement of the classified objects, which can be displayed on the display device.

Abstract: An adaptive real-time detection and examination network that employs deep learning to detect and recognize objects in a stream of pixilated two-dimensional digital images. The network provides the images from an image source as pixilated image frames to a CNN having an input layer and output layer, where the CNN identifies and classifies the objects in the image. The network also provides metadata relating to the image source and its location, and provides the object classification data and the metadata to an RNN that identifies motion and relative velocity of the classified objects in the images. The network combines the object classification data from the CNN and the motion data from the RNN, and correlates the combined data to define boundary boxes around each of the classified objects and an indicator of relative velocity and direction of movement of the classified objects, which can be displayed on the display device.

Abstract: A system for distributive training and weight distribution in a neural network. The system includes a training facility having a training neural network that detects and classifies objects in training images so as to train weights of nodes in the training neural network, and a plurality of object detection and classification units each including an image source that provides image frames, and at least one classification and prediction neural network that identifies, classifies and indicates relative velocity of objects in the image frames. Each unit transmits its image frames to the training facility so that the training neural network further trains the weights of the nodes in the training neural network, and the trained neural network weights are transmitted from the training facility to each of the object detection and classification units so as to train weights of nodes in the at least one classification and prediction neural network.

Abstract: A system for distributive training and weight distribution in a neural network. The system includes a training facility having a training neural network that detects and classifies objects in training images so as to train weights of nodes in the training neural network, and a plurality of object detection and classification units each including an image source that provides image frames, and at least one classification and prediction neural network that identifies, classifies and indicates relative velocity of objects in the image frames. Each unit transmits its image frames to the training facility so that the training neural network further trains the weights of the nodes in the training neural network, and the trained neural network weights are transmitted from the training facility to each of the object detection and classification units so as to train weights of nodes in the at least one classification and prediction neural network.