Comparative analysis of object tracking algorithms - Zalesky, B. A.; Ivanyukovich, V. A.; Reer, K. V.; Starikovich, D. A. (Informatika (Minsk, Belarus), 2025)
Objectives. The article presents the results of calculation and comparative analysis of the characteristics of the algorithm proposed by the authors in [1] for tracking an object captured by a video camera, when solving the urgent task of automatic detection and tracking of drones. Two algorithms were selected for comparative analysis, one of which is the currently known open source ByteTrack tracker, and the other is a simple tracker based on the use of the neural network, correlation comparison together with Kalman filter. The first tracker was chosen because it can be implemented in C++ without using third-party libraries and frameworks and used on small computers in real time. The second tracker was used to determine how much better new trackers are than simple, long-used ones. The specificity of the used algorithms is automatic detection and capture of the drone, its further reliable tracking, quick repeated capture in case of tracking failure, capture of another drone when the tracked object disappears. In the used trackers, drone detection in video frames is carried out using a neural network detector, and tracking is done with the help of the neural network detector and developed tracking algorithms. Methods. To perform a comparative analysis of object tracking algorithms, two datasets consisting of video frames that contain drone images were created and labeled. The training dataset consists of 36895 frames whereas testing one contains 8678 images. The videos of the training and test datasets were shot with different cameras in different conditions. To train the neural network part of the trackers, versions of the algorithms were written in the Python programming language, and to calculate and analyze characteristics in conditions close to real ones, in C++, which required converting the trained network using the TensorRT framework. Software tools for gathering and processing experimental data were also implemented. Results . The comparative analysis of three object tracking algorithms allowed us to calculate and compare the characteristics of these trackers, as well as draw conclusions about the method of training the used neural network detector; about the possibility of using trackers in real time on budget personal computers with budget video cards that have the CUDA software and hardware architecture, about the applicability of two of them for solving the problem of practical tracking of drones observed by video cameras with sufficient accuracy and reliability. Of the three tested algorithms the tracker previously developed by the authors has the best characteristics. Conclusion . The comparative analysis of the above-mentioned trackers showed the possibility of practical application of the tracker and the ByteTrack algorithm for solving the problem of tracking drones, however, there is still a problem with detecting small-sized unmanned aerial vehicles.