Object Tracking in SWIR Imaging Based on Both Correlation and Robust Kalman Filters

Pavlović, Miloš; Banjac, Zoran; Kovačevíc, Branko

doi:10.1109/access.2023.3288694

Cited by 1 publication

(1 citation statement)

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“…Reference [8] proposes an adaptive target tracking method based on ELM and KCF, which solves the problem of fast moving target tracking, but the tracking accuracy is not high under target occlusion. Reference [9] proposes an enhancement of the Kalman filter by introducing a nonlinear Huber influence function in the estimation step of the Kalman filter to address the issue of target tracking loss in occluded scenes. However, the robustness to outliers is reduced at the cost of reducing the efficiency of the estimator, resulting in a decrease in tracking accuracy.…”

Section: Introductionmentioning

confidence: 99%

Target Tracking Method Based on Kalman and Temporal Scale Adaptive Fusion CF

Zhang,

Xiao,

Wei

et al. 2024

Preprint

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Aiming at the problem of tracking drift and even loss caused by background occlusion in the tracking process of dim-small targets, a new tracking method combining Kalman and temporal scale adaptive fusion KCF (AKF-CF) is proposed.Firstly, extract the multi-modal features of the target grayscale image and directional gradient histogram (HOG), detect the target position, and improve the recognition ability of the target. Then the bilinear interpolation method is used to obtain a temporal scale adaptive box, and the size of the tracking box is dynamically adjusted. Using Average Peak Correlation Energy (APCE) for target occlusion detection and improving on KCF algorithm: if occlusion occurs, the Kalman filter algorithm is used to predict the target position at the next moment and replace the target in the original KCF algorithm to prevent tracking drift; If no occlusion occurs, the KCF algorithm continues to track, thus achieving effective tracking of targets in occluded environments. Using the OTB100 dataset for tracking long duration and occlusion sequences in videos. Compared with the ACSRCF algorithm on the OTB50 dataset, the accuracy and success rates improved by 0.8% and 0.06%, respectively. The data on the OTB100 dataset were 92.50% and 68.10%, respectively, surpassing other related filtering algorithms. Therefore, the success rate and tracking accuracy of the algorithm proposed in this paper have been significantly improved.

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Section: Introductionmentioning

confidence: 99%