Performance comparison of online and offline tracking algorithms

Ozan Yardimci; Ali Simsek Tekerek

doi:10.1117/12.2618461

31 May 2022 Performance comparison of online and offline tracking algorithms

Ozan Yardimci, Ali Simsek Tekerek

Proceedings Volume 12096, Automatic Target Recognition XXXII; 1209604 (2022) https://doi.org/10.1117/12.2618461
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

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.

Citation Download Citation

Ozan Yardimci and Ali Simsek Tekerek "Performance comparison of online and offline tracking algorithms", Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209604 (31 May 2022); https://doi.org/10.1117/12.2618461

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