AI Article Synopsis
- The study focuses on improving multi-target tracking in infrared fish-eye warning systems using a method called Neural Joint Probabilistic Data Association (NJPDA).
- It highlights that the NJPDA algorithm mainly uses distance for target tracking, which limits its accuracy, prompting the development of a new algorithm that incorporates directional information of targets.
- The proposed algorithm enhances tracking performance by modifying the likelihood function with a Gaussian weighting method, resulting in a nearly 10% increase in tracking success rate compared to existing algorithms while also meeting real-time processing requirements.
Article Abstract
The multi-target data association method is studied in order to realize multi-target tracking in infrared fish-eye warning system. The Neural Joint Probabilistic Data Association (NJPDA) algorithm is analyzed. It is found that the NJPDA algorithm only considers the distance information between the measurement and the target in the data association process, and its tracking accuracy needs to be further improved. Therefore, a new method fused with direction information is proposed based on the NJPDA algorithm. The proposed algorithm defines the concept of direction difference, introduces the direction information of target motion, and modifies the likelihood function by Gaussian weighting method, so as to fuse the direction information of target motion into the calculation of data interconnection probability. Experimental results demonstrate that the tracking success rate of the proposed algorithm is nearly 10 % higher than that of JPDA and NJPDA algorithms and its consuming time meets the real-time requirement of the infrared fish-eye warning system.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10716398 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2023.e22628 | DOI Listing |
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