Image and WLAN Bimodal Integration for Indoor User Localization

Redzic, Milan; Laoudias, Christos; Kyriakides, Ioannis

doi:10.1109/tmc.2019.2903044

Cited by 20 publications

(3 citation statements)

References 39 publications

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“…The feature points of Scale independent feature transform (SIFT) mainly adopt the local maximum value of image and scale space [ 22 ]. The method adopted by Speeded up robust feature (SURF) is like that of SIFT, but it mainly uses gaussian filter to response [ 23 ], so the computation efficiency is higher.…”

Section: Methodsmentioning

confidence: 99%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.

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Section: Methodsmentioning

confidence: 99%

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

2020

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“…The naive Bayes classification algorithm is based on Bayes' theorem and the assumption of conditional independence of features. Compared with the decision tree, the naive Bayes algorithm considers the prior probability and belongs to an active learning algorithm model [15]. Similar to the decision tree model, the naive Bayes model has a wide range of data processing and has no exact requirements for the type of data.…”

Section: Naive Bayes Classification Algorithmmentioning

confidence: 99%

Financial customer classification by combined model

Liu

Zheng

2022

Applied Mathematics and Nonlinear Sciences

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This paper explores the pros and cons of different algorithm models on the same selection problem, and then uses the combined prediction theory to obtain a new combined prediction model to explore its prediction accuracy. The actual problem to be solved is to help financial institutions to scientifically classify customers who choose financial products. We select the bank data set in the UCI database, which is derived from the survey data of a customer conducted by a financial institution in Portugal for a wealth management product. Decision tree C5.0 algorithm, naive Bayes classification algorithm and binary logit model are individually used to carry out a single model of empirical research on financial product customer classification. Through the empirical analysis of the five combination models, it is concluded that in the model that uses the least squares weighting method to determine the weight, the weight appears negative, which does not conform to the actual situation. The model that is based on the least squares weighting method and the model that is based on the simple weighting method are excluded. In contrast, the arithmetic mean weighted model is better than the reciprocal variance weighted model and the reciprocal mean square model. The accuracy reaches 89.91%, which is 0.43% higher than the accuracy of a single model. It can be concluded that the model that is based on the arithmetic average weighting is a better combination forecasting model.

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“…The method uses the exit signs in a building to calculate the image fingerprints and performs rough positioning via Wi-Fi fingerprint matching, image matching positioning, and refined positioning to obtain the final position estimation. Milan et al [ 35 ] discussed a merge strategy based on WLAN and images, using the extended naive Bayes method and a speeded-up robust features algorithm based on a hierarchical vocabulary tree to localize the WLAN and image, respectively; then, they proposed a particle filter position estimation method from the two perspectives of features and localization results. The filtering position estimation method had an improved adaptability to different scenes.…”

Section: Related Workmentioning

confidence: 99%

LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints

Zhang

2021

Sensors

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Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.

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Image and WLAN Bimodal Integration for Indoor User Localization

Cited by 20 publications

References 39 publications

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

Financial customer classification by combined model

LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints

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