A data processing system comprising a processor adapted to perform distributed training of neural network, said system comprising: a) a training facility including a tools suite that provides training images and object labels for objects in the training images, at least one training neural network that detects and classifies the objects in the training images so as to train weights of nodes in the at least one training neural network, and a training facility transceiver for distributing the trained neural network weights and receiving image frames; and b) a plurality of object detection and classification units each receiving the trained neural network weights from the training facility that have been trained by the training images, each object detection and classification unit including an image source that provides pixilated image frames of moving objects, at least one classification and prediction neural network that identifies, classifies and indicates relative velocity of the objects in the image frames, and a unit transceiver for transmitting the image frames to the training facility and receiving the trained neural network weights from the training facility, wherein each object detection and classification unit transmits its image frames to the training facility so as to allow the at least one training neural network to further train the weights of the nodes in the at least one training neural network, and wherein the trained neural network weights are distributed from the training facility by the training facility transceiver to each of the object detection and classification units, wherein weights of nodes are revised and updated in the at least one classification and prediction neural network in each object detection and classification units based upon objects in image frames from other object detection and classification units. a) a training facility including a tools suite that provides training images and object labels for objects in the training images, at least one training neural network that detects and classifies the objects in the training images so as to train weights of nodes in the at least one training neural network, and a training facility transceiver for distributing the trained neural network weights and receiving image frames; and b) a plurality of object detection and classification units each receiving the trained neural network weights from the training facility that have been trained by the training images, each object detection and classification unit including an image source that provides pixilated image frames of moving objects, at least one classification and prediction neural network that identifies, classifies and indicates relative velocity of the objects in the image frames, and a unit transceiver for transmitting the image frames to the training facility and receiving the trained neural network weights from the training facility, wherein each object detection and classification unit transmits its image frames to the training facility so as to allow the at least one training neural network to further train the weights of the nodes in the at least one training neural network, and wherein the trained neural network weights are distributed from the training facility by the training facility transceiver to each of the object detection and classification units, wherein weights of nodes are revised and updated in the at least one classification and prediction neural network in each object detection and classification units based upon objects in image frames from other object detection and classification units. an image source that provides pixilated image frames of moving objects, at least one classification and prediction neural network that identifies, classifies and indicates relative velocity of the objects in the image frames, and a unit transceiver for transmitting the image frames to the training facility and receiving the trained neural network weights from the training facility, wherein each object detection and classification unit transmits its image frames to the training facility so as to allow the at least one training neural network to further train the weights of the nodes in the at least one training neural network, and wherein the trained neural network weights are distributed from the training facility by the training facility transceiver to each of the object detection and classification units, wherein weights of nodes are revised and updated in the at least one classification and prediction neural network in each object detection and classification units based upon objects in image frames from other object detection and classification units.