Abstract: A method for automatically determining and using noise statistics of noisy observation data provided by at least one sensor connected to a tracker, in order to improve tracking of a time-varying object across a scene using a Kalman algorithm. The Kalman algorithm allowing to get an estimation of dynamic parameters of the object and providing a precision of said estimation from said noisy observation data and from a corrected previous estimation. The method includes receiving reference data from a data source external to the Kalman algorithm and connected to the tracker, deriving, from said reference data, said noise statistics reflecting errors made by the sensor, and using said noise statistics as setting parameters of the Kalman algorithm.

Abstract: A computer implemented method for determining candidate data sets for labelling comprises the following steps carried out by computer hardware components: determining a plurality of sensor data sets; determining a respective signature for each of the plurality of sensor data sets; determining, based on the signature of the respective sensor data set, for each of the plurality of sensor data sets whether the respective sensor data set is a candidate data set for labelling; and providing the sensor data set to a labeling instance for labelling if the sensor data set is a candidate data set for labelling.

Abstract: A computer implemented method for detecting objects includes providing signal representation data comprising range information, velocity information and angular information; for each of a plurality of spatial scales, determining respective scaled data for the respective spatial scale based on the signal representation data, to obtain a plurality of scaled data; providing the plurality of scaled data to a plurality of detectors; and each detector carrying out object detection based on at least one of the plurality of scaled data.