Retraction Note to: The visual object tracking algorithm research based on adaptive combination kernel

Chen, Yuantao; Jin, Wang; Xia, Runlong; Zhang, Qian; Cao, Zhouhong; Yang, Kai

doi:10.1007/s12652-021-03500-6

Cited by 5 publications

(4 citation statements)

References 0 publications

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“…The migrating target's velocity and propagation distance were calculated with motion estimation and SPD. The result of this approach seems to be same as that of the CAMShift algorithm, where the object moves with high random velocities and acceleration (Chen, Wang & Xia, 2019). (Ren et al, 2016) proposed a Region Proposal Network (RPN) based on Region-Based Convolutional Neural Networks (R-CNN) which was computationally expensive.…”

Section: Introductionmentioning

confidence: 86%

Moving Human Target Detection and Tracking in Video Frames

Nallasivam¹,

Senniappan²

2021

STUD INFORM CONTROL

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The conventional method for moving human target detection and tracking has come across a major setback due to various hindering factors such as environmental lighting conditions, temperature, etc. Similarly, it has been noticed that the manual selection of moving human targets in a video sequence does not provide convincing results either. In this paper, a new method for moving human target detection and tracking is proposed. It involves two stages. The first stage consists in the detection of moving human targets and the second one in target tracking based on the Continuously Adaptive Mean-Shift (CAMShift) algorithm. In the first stage, in order to select the moving target, the background subtraction method and frame subtraction method are combined. The Region Of Interest (ROI), which is usually the moving target is identified. In the second stage, target tracking is performed by choosing a centroid pixel point over the ROI, which is then used by the CAMShift algorithm. The proposed method has shown outperforming results for various performance parameters such as precision, accuracy, recall, and the F1-score under three different lighting conditions. The results obtained also show a reduction in time complexity in comparison with the state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 86%

Moving Human Target Detection and Tracking in Video Frames

Nallasivam¹,

Senniappan²

2021

STUD INFORM CONTROL

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“…An early technology system research of smart city is initiated by IBM researchers. Technical functions of Smarter CitiesTM, proposed by IBM [25][26][27], emphasize the importance of service and infrastructure as the center of smart cities. In the literature [28], from the point of view of integrated intelligence to understand the framework of the city, a smart city's initial framework was proposed.…”

Section: Technology System Of Smart Citymentioning

confidence: 99%

Long-term Preservation of Electronic Record Based on Digital Continuity in Smart Cities

Ren¹,

Zhu²,

Gao³

et al. 2021

Computers, Materials &Amp; Continua

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Under the co-promotion of the wave of urbanization and the rise of data science, smart cities have become the new concept and new practice of urban development. Smart cities are the combination of information technology represented by the Internet of Things, cloud computing, mobile networks and big data, and urbanization. How to effectively achieve the long-term preservation of massive, heterogeneous, and multi-source digital electronic records in smart cities is a key issue that must be solved. Digital continuity can ensure the accessibility, integrity and availability of information. The quality management of electronic record, like the quality management of product, will run through every phase of the urban lifecycle. Based on data quality management, this paper constructs digital continuity of smart city electronic records. Furthermore, the work in this paper ensures the authenticity, integrity, availability and timeliness of electronic documents by quality management of electronic record. This paper elaborates on the overall technical architecture of electronic record, as well as the various technical means needed to protect its four characteristics.

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“…New space, then use non-negative matrix factorization for features extraction and analysis in new spaces. On the other hand, kernel method does not require knowledge of the details of the data, only the connection between each data, and finally, the kernel method can handle those data points with negative characteristics [44,45].…”

Section: Kernel Non-negative Matrix Factorization (Knmf)mentioning

confidence: 99%

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

Li¹,

Qian²,

Ren³

et al. 2020

Computers, Materials &Amp; Continua

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The application field of the Internet of Things (IoT) involves all aspects, and its application in the fields of industry, agriculture, environment, transportation, logistics, security and other infrastructure has effectively promoted the intelligent development of these aspects. Although the IoT has gradually grown in recent years, there are still many problems that need to be overcome in terms of technology, management, cost, policy, and security. We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data. To avoid the leakage and loss of various user data, this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization, which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation. Compared to non-negative matrix factorization privacy-preserving algorithm, the new algorithm does not need to know the detailed information of the data, only need to know the connection between each data; and the new algorithm can process the data points with negative characteristics. Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users' personal privacy.

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Retraction Note to: The visual object tracking algorithm research based on adaptive combination kernel

Cited by 5 publications

References 0 publications

Moving Human Target Detection and Tracking in Video Frames

Moving Human Target Detection and Tracking in Video Frames

Long-term Preservation of Electronic Record Based on Digital Continuity in Smart Cities

Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing

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