Anti-occlusion object tracking based on correlation filter

Liu, Jun; Xiao, Gang; Zhang, Xingchen; Xiong, Xingzhong; Peng, Shengyun

doi:10.1007/s11760-019-01601-6

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…They shift the center of the region of interest to nonmaximum local peak positions in the response map and re-extract features to obtain a new response map. Liu et al [20] propose a reliable evaluation criterion for the tracking process based on the displacement difference between adjacent frames, PSR, and response value. In the re-detection stage, the EdgeBox [21] method is employed to detect potential objects.…”

Section: Related Workmentioning

confidence: 99%

Enhanced correlation filter tracking: anti-occlusion and re-detection

Li,

Wei,

Zhang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Target tracking in computer vision is a challenging task that requires both accuracy and robustness, especially in the presence of occlusions and complex backgrounds. To tackle these challenges, this paper proposes a novel tracking approach based on correlation filters, which incorporates reliability assessment and re-detection modules. The proposed approach leverages enhanced color histogram scores in combination with correlation filter response maps to improve the accuracy of target localization. By considering metrics such as maximum response value, average peak-to-correlation energy, and peak-to-sidelobe ratio, the reliability of the tracking process is evaluated, providing insights into its performance and stability. In cases of tracking failure, a re-detection module is activated to reacquire the lost target and resume the tracking task. This module dynamically adjusts and updates the model to enhance the accuracy and robustness of the tracking process. Extensive experiments are conducted on popular benchmark datasets includingOTB50, OTB100, as well as custom datasets. The results demonstrate the superiority of the proposed approach, particularly in challenging scenarios with occlusions and complex scenes, outperforming baseline trackers and other state-of-the-art tracking methods.

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Section: Related Workmentioning

confidence: 99%

Enhanced correlation filter tracking: anti-occlusion and re-detection

Li,

Wei,

Zhang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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show abstract

“…Gao et al proposed a maximum margin object tracking with weighted circulant feature maps (MMWCF) [41] to reduce the influence of inaccurate samples. An anti-occlusion correlation filter-based tracking method (AO-CF) [42] proposed an occlusion criterion and a new detection condition for detecting proposals. Long-term correlation tracking (LCT) [43] consists of translation and scale estimation and trains an online random fern classifier to re-detect objects in case of tracking failure.…”

Section: The Correlation Filter-based Object Tracking Algorithmsmentioning

confidence: 99%

Real-Time Object Tracking via Adaptive Correlation Filters

Lan

Gao

et al. 2020

Sensors

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Although correlation filter-based trackers (CFTs) have made great achievements on both robustness and accuracy, the performance of trackers can still be improved, because most of the existing trackers use either a sole filter template or fixed features fusion weight to represent a target. Herein, a real-time dual-template CFT for various challenge scenarios is proposed in this work. First, the color histograms, histogram of oriented gradient (HOG), and color naming (CN) features are extracted from the target image patch. Then, the dual-template is utilized based on the target response confidence. Meanwhile, in order to solve the various appearance variations in complicated challenge scenarios, the schemes of discriminative appearance model, multi-peaks target re-detection, and scale adaptive are integrated into the proposed tracker. Furthermore, the problem that the filter model may drift or even corrupt is solved by using high confidence template updating technique. In the experiment, 27 existing competitors, including 16 handcrafted features-based trackers (HFTs) and 11 deep features-based trackers (DFTs), are introduced for the comprehensive contrastive analysis on four benchmark databases. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art HFTs and is comparable with the DFTs.

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Robust visual tracking for UAVs with dynamic feature weight selection

Wang

et al. 2022

Appl Intell

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Anti-occlusion object tracking based on correlation filter

Cited by 6 publications

References 27 publications

Enhanced correlation filter tracking: anti-occlusion and re-detection

Enhanced correlation filter tracking: anti-occlusion and re-detection

Real-Time Object Tracking via Adaptive Correlation Filters

Robust visual tracking for UAVs with dynamic feature weight selection

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