Detecting, identifying, and tracking objects based upon a tensor with sparsely computed non-zero values

Abstract

Technologies for detecting, identifying, and tracking objects in a scene based upon images generated by a camera are described. A computing system obtains a perspective image from the camera and generates a feature image based upon the perspective image. The feature image has X×Y pixels, and each pixel in the X×Y pixels has a feature vector that includes values assigned thereto. The feature image is provided as input to a machine learning model, where the machine learning model generates a tensor based upon the feature image, where the tensor includes several bins that include values that are representative of likelihoods that an object exists in the scene at distances from the camera that correspond to the bins. The machine learning model is trained using a Gumbel-softmax algorithm. The computing system generates the image in the overhead view based upon the tensor output by the machine learning model.