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Objectives. Sports events are currently among the most promising areas for the application of tracking systems. In most cases, such systems are designed to track moving objects in a two-dimensional plane, e.g., players on the field, as well as to identify them by various features. However, as new sports such as drone racing are developed, the problem of determining the position of an object in a three-dimensional coordinate system becomes relevant. The aim of the present work was to develop algorithms and software for a method to perform 3D tracking of moving objects, regardless of the data segmentation technique, and to test this method to estimate the tracking quality.Methods. A method for matching information on the speed and position of objects was selected based on a review and analysis of contemporary tracking methods.Results. The structure of a set of algorithms comprising software for a moving-object tracker for sports events is proposed. Experimental studies were performed on the publicly available APIDIS dataset, where a MOTA metric of 0.858 was obtained. The flight of an FPV quadcopter along a track was also tracked according to the proposed dataset; the 3D path of the drone flight was reconstructed using the tracker data.Conclusions. The results of the experimental studies, which demonstrated the feasibility of using the proposed method to track a quadcopter flight trajectory in a three-dimensional world coordinate system, is also showed that the method is suitable for tracking objects at sports events.

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The paper proposes a novel approach to the objects localization in the working area of a modular reconfigurable robot (MRR), which implies the installation of stationary monitoring points (SMP), consisting of detachable robot’s modules and in- stalled by robot itself. This approach is based on the architecture of the MRR control system previously proposed by the authors and a new method for comparing information about the speed and position obtained from various sensors. The key steps of the approach are following. Upon arriving in the target area, the MRR places SMPs, which consist of a power source, a computing device, a wireless transceiver and a sensor, detached from the robot. Then SMPs monitor the working area using different types of sensors (cameras, rangefinders, etc.), perform segmentation of the measured data and transfer this information to the robot. Further a sensor fusion is performed using a novel object tracking method, which makes it possible to localize target objects even in those cases when they are not visible by some of the SMPs. One of the key advantages of the new approach is a possibility of implementation in the distributed architecture of a MRR. The simulation results show that proposed method has Multiple Object Tracking Accuracy (MOTA) metric of 86 %, which is higher than the most of its analogues, while the estimated dynamic object localization error in a 8x7 m working area using 2 cameras and 1 rangefinder does not exceed 10 cm.