The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring
Open accessAbstract
In the era of rapid development of artificial intelligence, the integration of multimedia and human-artificial intelligence has become an important research hotspot. Especially in the multimedia environment, effective remote visual monitoring has become the exploration direction of many scholars. The use of traditional correlation filtering (CF) algorithm for real-time monitoring in the context of multimedia is a practical strategy. However, most existing filtering-based visual monitoring algorithms still have the problem of insufficient robustness and effectiveness. Therefore, by considering the strategy of updating human memory, this paper proposes a multi-layer template update mechanism to achieve effective monitoring in a multimedia environment. In this strategy, the weighted template of the high-confidence matching memory is used as the confidence memory, and the unweighted template of the low-confidence matching memory is used as the cognitive memory. Through the alternate use of confidence memory, matching memory, and cognitive memory, it is ensured that the target will not be lost during the monitoring process. Experimental results show that this strategy does not affect the speed (still real-time) and improves the robustness in the multimedia background.
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Cites
Online Object Tracking: A Benchmark
Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138
Visual object tracking using adaptive correlation filters
Bolme, D., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539960 · Cited by 3,406
Incremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061
Exploiting the Circulant Structure of Tracking-by-Detection with Kernels
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33765-9_50 · Cited by 2,310
Accurate Scale Estimation for Robust Visual Tracking
Danelljan, M., Häger, G., Shahbaz Khan, F., & Felsberg, M. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.65 · Cited by 2,087
Staple: Complementary Learners for Real-Time Tracking
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., & Torr, P. H. S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.156 · Cited by 1,776
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742
Discriminative Scale Space Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2609928 · Cited by 1,315
Discriminative Correlation Filter with Channel and Spatial Reliability
Lukezic, A., Vojir, T., Zajc, L. C., Matas, J., & Kristan, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.515 · Cited by 1,153
Deep learning in video multi-object tracking: A survey
Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665
Cited by
A Pattern Recognition Method of Personalized Adaptive Learning in Online Education
Peng, P., & Fu, W. (2022) · Mobile Networks and Applications · doi:10.1007/s11036-022-01942-6 · Cited by 29
Metadata
- Authors
- Liu, S., Wang, S., Liu, X., Gandomi, A. H., Daneshmand, M., Muhammad, K., & De Albuquerque, V. H. C.
- Venue
- IEEE Transactions on Multimedia
- Published
- 2021
- Volume
- 23
- Pages
- 2188-2198
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W3137539129
- Cited by
- 253
- References
- 25
- FWCI
- 21.25
- Type
- journal article
- Language
- en
- Open access
- green
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- Liu, S., Wang, S., Liu, X., Gandomi, A. H., Daneshmand, M., Muhammad, K., & De Albuquerque, V. H. C. (2021). Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring. IEEE Transactions on Multimedia. https://doi.org/10.1109/tmm.2021.3065580
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