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Conventional cameras provide images where the intensity of a pixel represents the amount of light detect by the sensor. Event-based vision is an emerging technology wherein computer algorithms process imagery obtained from a camera and provide time-stamped images where pixel intensity corresponds to dynamic changes in the images and how recent the change has been. Such images can be used as an input to a convolutional neural network to detect and predict events.
Existing methods require the use training datasets to train convolutional neural networks that can subsequently predict motion. This process is computationally intense and requires the method to discard image frames for real-time applications, thereby reducing the temporal resolution of the event detection.
Researchers at the University of Pennsylvania have developed an algorithm that can use a self-supervised convolutional neutral network to detect motion and predict an indication of an event from images obtained using one or multiple cameras. The method uses a multilayer convolutional neural network to detect motion and flow of the pixels at different spatial resolutions. The images obtained from multiple cameras can be used in the algorithm to more accurately associate discretized time stamps to each change, and ultimately improve the temporal resolution of event detection and prediction.