Comparative evaluations of online and offline visual tracking algorithms

Ozan Yardimci

doi:10.1117/12.2638228

28 October 2022 Comparative evaluations of online and offline visual tracking algorithms

Ozan Yardimci

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 122760B (2022) https://doi.org/10.1117/12.2638228
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

In this study, the performance of online and offline tracking algorithms, which are frequently used in the literature, were compared on the defined datasets. Therefore, six different datasets are prepared. The datasets consist of consecutive frames. In each dataset, target has different motion characteristics and the background types are different from each other. In addition, a total of six well known algorithms were used for comparison. These are KCF, MOSSE, CSRT, TLD, Go Turn and Siamese tracking algorithms. In conclusion, especially in cases where the target is small and the SNR value is low, the highest performance is obtained with the KCF algorithm. On the other hand, when the target is big and the SNR value is high, it is observed that the Siamese algorithm better handle changes in the target shape. In this context, considering the real scenarios, it may be possible to use the algorithms in a hybrid way to get the better performance.

Conference Presentation

Citation Download Citation

Ozan Yardimci "Comparative evaluations of online and offline visual tracking algorithms", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 122760B (28 October 2022); https://doi.org/10.1117/12.2638228

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