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Firstly, the multi-layer feature fusion module for template branch (MFFMT) and the multi-layer feature fusion module for search branch (MFFMS) are designed. The modified convolutional neural networks (CNN) are used for feature extraction through the fusion module to solve the problem of features loss caused by too deep network. Secondly, an attention network is introduced into the SiamANAL algorithm to calculate the attention of template map features and search map features, which enhances the features of object region, reduces the interference of background region, and improves the accuracy of the algorithm. Finally, an adaptive loss function combined with pairwise Gaussian loss function and cross entropy loss function is designed to increase inter-class separation and intra-class compactness of classification branches and improve the accuracy rate of classification and the success rate of regression. The effectiveness of the proposed algorithm is verified by comparing it with other popular algorithms on two popular benchmarks, the visual object tracking 2018 (VOT2018) and the object tracking benchmark 100 (OTB100). Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The success rate and precision rate of the proposed algorithm SiamANAL on OTB100 are 0.709 and 0.883, respectively. With the help of cloud computing services and data storage, the processing performance of the proposed algorithm can be further improved.<\/jats:p>","DOI":"10.1186\/s13677-023-00431-9","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T07:09:37Z","timestamp":1680592177000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A cloud-oriented siamese network object tracking algorithm with attention network and adaptive loss function"],"prefix":"10.1186","volume":"12","author":[{"given":"Jinping","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"issue":"7","key":"431_CR1","doi-asserted-by":"publisher","first-page":"4389","DOI":"10.1007\/s11276-021-02664-5","volume":"27","author":"D Li","year":"2021","unstructured":"Li D, Bei LL, Bao JN, Yuan SZ, Huang K (2021) Image contour detection based on improved level set in complex environment. 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