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This work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and tested on the basis of real, multimodal data containing synchronized video sequences and precise motion capture data as a ground truth reference. The bounding boxes are determined based on the 3D position and orientation of an asymmetric cross attached to the top of the tracked object with known translation to the object's center. The arms of the cross are identified by the markers registered by motion capture acquisition. Besides the classical mean average precision (mAP), a measure more adequate in the evaluation of detection performance in 3D tracking is proposed, namely the average distance between the centroids of matched references and detected drones, including false positive and false negative ratios. Moreover, the videos generated in the AirSim simulation platform were taken into account in both the training and testing stages. [ABSTRACT FROM AUTHOR]

The search for clouds in satellite images is a challenging subject which still attracts a lot of attention due to the amount and quality of data, which is growing at a tremendous pace, the development of satellite techniques and methods, inexpensive equipment, and automation of satellite imaging processes. This paper presents a new approach to the assessment of cloudiness based on the use of the theory of moments with invariants. The values of moments with invariants, determined on the basis of the available cloudiness maps, create a new, valuable set of data, which are the geometrical parameters of the scene representing the cloud cover. In further research, the obtained data sets will be used in machine learning methods, deep machine learning methods, etc. The method is used for different conditions, including different angular positions of the Sun and time periods. The effectiveness of the method is checked on the basis of comparing the entropy results of the input maps after subtracting clouds masked by various methods. The obtained results additionally indicate the potential of the moments method as a support for the existing methods of estimating cloudiness over the sea surface. [ABSTRACT FROM AUTHOR]