Abstract: Technologies and techniques for automatically executing a security function of a surveillance system with respect to at least one object of interest based on spatially resolved sensor data for environment perception generated by at least one sensor device. Spatially resolved sensor data, is received. A trained machine-learning computer vision model is accessed to determine at least one surveillance parameter. The model includes a set of parameters set to values learned as a result of a training process based on a synthetic training data set, which is generated by rendering at least one model of the at least one object of interest according to at least one render parameter. The generation of the training data set is determined based on at least one environment parameter being characteristic for the environment to be surveilled and/or for the sensor device. The security function is executed depending on the at least one surveillance parameter.

Abstract: A computer-implemented method for generating a synthetic training data set for training a machine learning computer vision model for performing at least one user defined computer vision task, in which spatially resolved sensor data are processed and evaluated with respect to at least one user defined object of interest, including receiving at least one model of a user defined object of interest; determining at least one render parameter and multiple render parameters; generating a set of training images by rendering the at least one model of the object of interest based on the at least one render parameter; generating annotation data for the set of training images with respect to the at least one object of interest; and providing a training data set including the set of training images and the annotation data for being output to the user and/or for training the computer vision model.