Robust fusion for RGB-D tracking using CNN features

Wang, Yong; Wei, Xian; Shen, Hao; Ding, Lu; Wan, Jiuqing

doi:10.1016/j.asoc.2020.106302

Cited by 22 publications

(12 citation statements)

References 39 publications

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“…The hidden layers usually consist of convolutional layers, ReLU layers, pooling layers, and fully connected layers [35] , [36] . CNN’s represents a huge breakthrough in automatic image classification systems as there is no need for pre-processing the images, that was needed in traditional machine learning algorithms [37] , [38] , [39] .…”

Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

293

151

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COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors’ knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.

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Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

293

151

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“…This has drawn the attention of scholars focusing on how to introduce depth information into a target tracking algorithm and improve its performance. For example, Wang Y. et al ( 2020 ) proposed a robust fusion-based RGB-D tracking method that integrates depth data into a visual object tracker to achieve the robust tracking of a target. In addition, Xiao et al ( 2019 ) proposed a new tracking method that uses a kernel support vector machine (SVM) online learning classifier to detect and track a specific target in a single RGB-D sensor.…”

Section: Introductionmentioning

confidence: 99%

Introducing Depth Information Into Generative Target Tracking

et al. 2021

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Common visual features used in target tracking, including colour and grayscale, are prone to failure in a confusingly similar-looking background. As the technology of three-dimensional visual information acquisition has gradually gained ground in recent years, the conditions for the wide use of depth information in target tracking has been made available. This study focuses on discussing the possible ways to introduce depth information into the generative target tracking methods based on a kernel density estimation as well as the performance of different methods of introduction, thereby providing a reference for the use of depth information in actual target tracking systems. First, an analysis of the mean-shift technical framework, a typical algorithm used for generative target tracking, is described, and four methods of introducing the depth information are proposed, i.e., the thresholding of the data source, thresholding of the density distribution of the dataset applied, weighting of the data source, and weighting of the density distribution of the dataset. Details of an experimental study conducted to evaluate the validity, characteristics, and advantages of each method are then described. The experimental results showed that the four methods can improve the validity of the basic method to a certain extent and meet the requirements of real-time target tracking in a confusingly similar background. The method of weighting the density distribution of the dataset, into which depth information is introduced, is the prime choice in engineering practise because it delivers an excellent comprehensive performance and the highest level of accuracy, whereas methods such as the thresholding of both the data sources and the density distribution of the dataset are less time-consuming. The performance in comparison with that of a state-of-the-art tracker further verifies the practicality of the proposed approach. Finally, the research results also provide a reference for improvements in other target tracking methods in which depth information can be introduced.

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“…It should be pointed out that most of the proposals use Red Green Blue Depth (RGB-D) cameras to detect people in the environment. For instance, the authors in [2] propose a solution based on a RGB-D camera combining RGB and depth data to gather input data for a segmentation CNN. Other researchers combine data from several sensors.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

Álvarez-Aparicio¹,

Guerrero‐Higueras²,

Calderita³

et al. 2021

Applied Sciences

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Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people’s location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one—where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%.

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Robust fusion for RGB-D tracking using CNN features

Cited by 22 publications

References 39 publications

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Introducing Depth Information Into Generative Target Tracking

Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

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