SELECTION AND TESTING OF A VISUAL OBJECT TRACKING METHOD FOR EMBEDDED SYSTEMS - VERHUN, KOSTIANTYN; DYVAK, MYKOLA (Herald of Khmelnytskyi National University. Technical sciences, 2025)
This paper considers a task of visual object tracking with application in embedded systems with limited computational power. The complexity of the task lies in finding the proper balance between algorithm result quality and its runtime efficiency. To select the tracking method to be used in embedded systems multiple machine learning approaches were analyzed: convolutional neural network-based methods, recurrent neural network-based methods, one-shot learning-based methods, siamese neural networks and transformers. The method nanotrack was selected for further research and analysis. To perform runtime performance and algorithm accuracy evaluation the test environment was developed using C++ programming language which was deployed on Raspberry Pi hardware. Performance analysis showed the method is suitable for real time object tracking on Raspberry Pi 5 as it allows to process more than 25 frames per second, additionally leaving the room for usage of a more complex backbone model if needed. Accuracy was evaluated using LaSOT dataset. The main types of challenging situations for the tracking algorithm were identified and corresponding videos from LaSOT dataset were selected. Based on visually observing tracking results on these videos, the conclusions about overall sufficient adaptive qualities of the algorithm were made. To evaluate overall algorithm quality the accuracy metric was calculated on LaSOT dataset and compared to other state-of-the-art tracking methods. Accuracy metrics were computed on LaSOT dataset, showing good performance for some object classes and worse scores for others. Based on the analysis, it is expected that training neural network on a data representing limited target set of object classes will lead to satisfactory accuracy for these classes while preserving original runtime performance. The further research directions are experimenting with different backbone models for the Nanotrack method and evaluating their influence on accuracy and runtime performance.
Keywords: artificial neural networks; visual object tracking; image processing