RETRACTED ARTICLE: Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance

Kim, Bubryur; Yuvaraj, N.; Preethaa, K. R. Sri; Santhosh, R.; Sabari, A.

doi:10.1007/s00500-020-04999-1

Cited by 48 publications

(22 citation statements)

References 57 publications

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“…e subsampling layer samples and extracts through the feature layer to reduce the difficulty of training and learning, greatly reducing the feature layer and making the training process more stable [21]. e definition of subsampling layer is shown in the following equation:…”

Section: Extraction Of Landscape Informationmentioning

confidence: 99%

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

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Section: Extraction Of Landscape Informationmentioning

confidence: 99%

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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“…We define a feature as a point that is a local maximum on a 3x3 area and is above a threshold. Also the results of the proposed method are compared with a tracking method that is implemented based on a kind of deep neural network called a Convolutional Neural Networks (CNN) [48]. This framework uses CNN to detect objects within the input frames.…”

Section: Simulation Resultsmentioning

confidence: 99%

Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

Dadgar

Baleghi

Ezoji

2021

IJE

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In video surveillance, multiple objects tracking (MOT) is a challenging task due to object matching problem in consecutive frames. The present paper aims to propose an improved object matching approach in MOT based on Zernike moments and combination of multiple similarity distance metrics. In this work, the object is primarily detected using background subtraction method while the Gaussian Mixture Model (GMM) is applied for object extraction in the next frames. Subsequently, the color histogram and the magnitude of Zernike moments of the objects are calculated. In the next step, the objects are matched in the current and the previous frames based on the Hausdorff distance between objects, Earth Mover's distance (EMD) between their color histograms, and Chi-square distance between their Zernike moments. Then, a voting mechanism is designed to find the best consensus object matching from the aforementioned metrics. Eventually, the location of each object is predicted by the Kalman filter to continue tracking in subsequent frames. The results show that the object tracking and matching performance is improved using the proposed method in the video sequences of the multi-camera pedestrian "EPFL" video dataset. Specifically, errors caused by the merging of targets are reduced in the proposed tracking process.

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“…ARN models find it difficult to operate on the missed data and impute it with meaningful time series values. Deep neural network (DNN) integrated with long short-term memory (LSTM) is used for the regression analysis to analyze the time series data [ 48 , 49 ]. A deep learning-based autoencoder network is implemented for landslide susceptibility prediction [ 50 ].…”

Section: Related Workmentioning

confidence: 99%

Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network

Kim

Yuvaraj

Preethaa

et al. 2021

Sensors

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Wind tunnel testing techniques are the main research tools for evaluating the wind loadings of buildings. They are significant in designing structurally safe and comfortable buildings. The wind tunnel pressure measurement technique using pressure sensors is significant for assessing the cladding pressures of buildings. However, some pressure sensors usually fail and cause loss of data, which are difficult to restore. In the literature, numerous techniques are implemented for imputing the single instance data values and data imputation for multiple instantaneous time intervals with accurate predictions needs to be addressed. Thus, the data imputation capacity of machine learning models is used to predict the missing wind pressure data for tall buildings in this study. A generative adversarial imputation network (GAIN) is proposed to predict the pressure coefficients at various instantaneous time intervals on tall buildings. The proposed model is validated by comparing the performance of GAIN with that of the K-nearest neighbor and multiple imputations by chained equation models. The experimental results show that the GAIN model provides the best fit, achieving more accurate predictions with the minimum average variance and minimum average standard deviation. The average mean-squared error for all four sides of the building was the minimum (0.016), and the average R-squared error was the maximum (0.961). The proposed model can ensure the health and prolonged existence of a structure based on wind environment.

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RETRACTED ARTICLE: Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance

Cited by 48 publications

References 57 publications

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

Wind-Induced Pressure Prediction on Tall Buildings Using Generative Adversarial Imputation Network

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