Design of a Traffic Surveillance Application using iFogSim
- Authors: Sinqandu, Mluleki
- Date: 2020
- Subjects: Cloud computing
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/18676 , vital:42705
- Description: __iFogSim is a toolkit to model, simulate and evaluate networks of Fog computing, Edge computing and Internet of Things (IoT). This framework provides the capabilities of analysing and evaluating the performance of applications and resource management policies in Fog/IoT environments, based on which designers can model and test their applications. This thesis proposes a novel application model of a traffic surveillance vehicular network application through smart cameras using iFogSim, where the scenario of multiple vehicles tracking is considered. The effectiveness of the proposed application model is assessed and validated by simulations using a modified application model inherited from a case study of intelligent surveillance through distributed camera networks introduced. Simulations are conducted using the iFogSim tool and performance evaluation is done. The comparison between one vehicle and multiple vehicle tracking is done and the results demonstrate that the multiple vehicle application model achieves a better performance in terms of average latency and data transfer rate
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- Authors: Sinqandu, Mluleki
- Date: 2020
- Subjects: Cloud computing
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/18676 , vital:42705
- Description: __iFogSim is a toolkit to model, simulate and evaluate networks of Fog computing, Edge computing and Internet of Things (IoT). This framework provides the capabilities of analysing and evaluating the performance of applications and resource management policies in Fog/IoT environments, based on which designers can model and test their applications. This thesis proposes a novel application model of a traffic surveillance vehicular network application through smart cameras using iFogSim, where the scenario of multiple vehicles tracking is considered. The effectiveness of the proposed application model is assessed and validated by simulations using a modified application model inherited from a case study of intelligent surveillance through distributed camera networks introduced. Simulations are conducted using the iFogSim tool and performance evaluation is done. The comparison between one vehicle and multiple vehicle tracking is done and the results demonstrate that the multiple vehicle application model achieves a better performance in terms of average latency and data transfer rate
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Developing a Machine Learning Algorithm for Outdoor Scene Image Segmentation
- Authors: Zangwa, Yamkela
- Date: 2020
- Subjects: Computational intelligence Computer science
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/12087 , vital:39150
- Description: Image segmentation is one of the major problems in image processing, computer vision and machine learning fields. The main reason for image segmentation existence is to reduce the gap between computer vision and human vision by training computers with different data. Outdoor image segmentation and classification has become very important in the field of computer vision with its applications in woodland-surveillance, defence and security. The task of assigning an input image to one class from a fixed set of categories seem to be a major problem in image segmentation. The main question that has been addressed in this research is how outdoor image classification algorithms can be improved using Region-based Convolutional Neural Network (R-CNN) architecture. There has been no one segmentation method that works best on any given problem. To determine the best segmentation method for a certain dataset, various tests have to be done in order to achieve the best performance. However deep learning models have often achieved increasing success due to the availability of massive datasets and the expanding model depth and parameterisation. In this research Convolutional Neural Network architecture is used in trying to improve the implementation of outdoor scene image segmentation algorithms, empirical research method was used to answer questions about existing image segmentation algorithms and the techniques used to achieve the best performance. Outdoor scene images were trained on a pre-trained region-based convolutional neural network with Visual Geometric Group-16 (VGG-16) architecture. A pre-trained R-CNN model was retrained on five different sample data, the samples had different sizes. Sample size increased from sample one to five, to increase the size on the last two samples the data was duplicated. 21 test images were used to evaluate all the models. Researchers has shown that deep learning methods perform better in image segmentation because of the increase and availability of datasets. The duplication of images did not yield the best results; however, the model performed well on the first three samples.
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- Authors: Zangwa, Yamkela
- Date: 2020
- Subjects: Computational intelligence Computer science
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/12087 , vital:39150
- Description: Image segmentation is one of the major problems in image processing, computer vision and machine learning fields. The main reason for image segmentation existence is to reduce the gap between computer vision and human vision by training computers with different data. Outdoor image segmentation and classification has become very important in the field of computer vision with its applications in woodland-surveillance, defence and security. The task of assigning an input image to one class from a fixed set of categories seem to be a major problem in image segmentation. The main question that has been addressed in this research is how outdoor image classification algorithms can be improved using Region-based Convolutional Neural Network (R-CNN) architecture. There has been no one segmentation method that works best on any given problem. To determine the best segmentation method for a certain dataset, various tests have to be done in order to achieve the best performance. However deep learning models have often achieved increasing success due to the availability of massive datasets and the expanding model depth and parameterisation. In this research Convolutional Neural Network architecture is used in trying to improve the implementation of outdoor scene image segmentation algorithms, empirical research method was used to answer questions about existing image segmentation algorithms and the techniques used to achieve the best performance. Outdoor scene images were trained on a pre-trained region-based convolutional neural network with Visual Geometric Group-16 (VGG-16) architecture. A pre-trained R-CNN model was retrained on five different sample data, the samples had different sizes. Sample size increased from sample one to five, to increase the size on the last two samples the data was duplicated. 21 test images were used to evaluate all the models. Researchers has shown that deep learning methods perform better in image segmentation because of the increase and availability of datasets. The duplication of images did not yield the best results; however, the model performed well on the first three samples.
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