A Novel Data Association Method for Multi-target Tracking Based on IACA
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36625-3_6
Reference18 articles.
1. Chen, T., Yang, P., Peng, H., Qian, Z.: Multi-target tracking algorithm based on PHD filter against multi-range-false-target jamming. J. Syst. Eng. Electron. 31(5), 859–870 (2020). https://doi.org/10.23919/JSEE.2020.000066
2. Wang, Z., Sun, J., Li, Q., Ding, G.: A new multiple hypothesis tracker integrated with detection processing. Sensors 19, 5278 (2019). https://doi.org/10.3390/s19235278
3. Tian, Y.C., Dehghan, A., Shah, M.: On detection, data association and segmentation for multi-target tracking. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2146–2160 (2019). https://doi.org/10.1109/TPAMI.2018.2849374
4. Zhao, S., Huang, Y., Wang, K., Chen, T.: Multi-source data fusion method based on nearest neighbor plot and track data association. In: 2021 IEEE Sensors, pp. 1–4. IEEE (2021)
5. Mbelwa, J.T., Zhao, Q., Lu, Y., Liu, H., Wang, F., Mbise, M.: Objectness-based smoothing stochastic sampling and coherence approximate nearest neighbor for visual tracking. Vis. Comput. 35(3), 371–384 (2018). https://doi.org/10.1007/s00371-018-1470-5
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