[Retracted] Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

Li, Zhengze; Jia-dong, XU

doi:10.1155/2021/4276860

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…With the introduction of the artificial neural network model [7][8][9][10], some scholars have optimized and improved the neural network model, and the proposed tracking algorithm has been greatly improved in accuracy and speed. In 2016, Valmadre et al proposed SiamFC [11], which trains a Siamese network to locate a sample image in a large search area to solve the similarity learning problem.…”

Section: Introductionmentioning

confidence: 99%

Research on Background Learning Correlation Filtering Algorithm With Multi-Feature Fusion

2023

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Aiming at the problems of occlusion, drift, and background change in target tracking, a background learning correlation filtering algorithm based on multi-feature fusion is proposed. In the framework of correlation filtering, multi-feature fusion, multi-template update, and background learning regularization are used to improve the performance of the filter in the problem of template contamination and object occlusion. The fast directional gradient histogram (FHOG), color feature (CN), and texture feature (ULBP) were extracted, and the feature channels were connected in series. Then the depth features of Conv4-4 and Conv5-4 layers were extracted through the VGG-19 network, and the appearance model of the target was constructed. To reduce the sensitivity of the filter to the sudden change of background, a background learning filter is constructed, and the alternate direction multiplier method (ADMM) is used to speed up the calculation of the filter. In the model update stage, aiming at the problem of pollution of the original template caused by target occlusion, a high-confidence multi-template fusion update strategy is proposed by fusing the template with the highest confidence in the current frame, the previous frame, and the history frame. Finally, the proposed algorithm is tested on OTB50, OTB100, UAV123, and TC128 experimental data sets, and some classical and latest algorithms. The experimental results show that the tracking accuracy and robustness of the correlation filtering algorithm are improved.INDEX TERMS Target tracking, Machine vision, Correlation filtering, Background learning, More templates.

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Section: Introductionmentioning

confidence: 99%

Research on Background Learning Correlation Filtering Algorithm With Multi-Feature Fusion

2023

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“…Human tracking aims to predict and locate the human's position in subsequent frames based on the initial human position in the first frame of the video, so as to estimate the movement track of the tracked human. After decades of development, human tracking technology has been widely used in the fields of security surveillance, human-robot interactions, sporting events, traffic monitoring [7][8][9][10], and has made great progress.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Vision-Based Tracking Method for Underground Human Using Infrared Videos

Wang

Chen

et al. 2022

Symmetry

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The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes.

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“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

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confidence: 99%

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

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confidence: 99%