INDOOR POSITIONING USING CONVOLUTION NEURAL NETWORK TO REGRESS CAMERA POSE

<p><strong>Abstract.</strong> In recent years, the issue of indoor positioning has become more and more popular and attracted more attention. Under the absence of GNSS, how to more accurately position is one of the challenges on the positioning technology. Camera positioning can be calculated by image and objects. Therefore, this study focuses on locating the user's camera position, but how to calculate the camera position efficiently is a very challenging problem. With the rapid development of neural network in image recognition, computer can not only process images quickly, but also achieve good results. Convolution Neural Network (CNN) can sense the local area of the image and find some high-resolution local features. These basic features are likely to form the basis of human vision and become an effective means to improve the recognition rate. We use a 23-layer convolutional neural network architecture and set different sizes of input images to train the end-to-end task of location recognition to regress the camera's position and direction. We choose the sites where are the underground parking lot for the experiment. Compared with other indoor environments such as chess, office and kitchen, the condition of this place is very severe. Therefore, how to design algorithms to train and exclude dynamic objects using neural networks is very exploratory. The experimental results show that our proposed solution can effectively reduce the error of indoor positioning.</p>

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Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

Measurement and Control ◽

10.1177/0020294020964826 ◽

2020 ◽

pp. 002029402096482

Author(s):

Sulaiman Khan ◽

Abdul Hafeez ◽

Hazrat Ali ◽

Shah Nazir ◽

Anwar Hussain

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Research Work ◽

Recognition Rate ◽

Recognition Task ◽

Convolution Neural Network ◽

Medium Size ◽

Support Vector ◽

Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

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A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

PLoS ONE ◽

10.1371/journal.pone.0250782 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0250782

Author(s):

Bin Wang ◽

Bin Xu

Keyword(s):

Neural Network ◽

Feature Fusion ◽

Rapid Development ◽

Vehicle Detection ◽

Convolution Neural Network ◽

Aerial Images ◽

Semantic Features ◽

General Object ◽

High Level ◽

The Impact

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

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Integration of Convolutional Neural Network and Error Correction for Indoor Positioning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020074 ◽

2020 ◽

Vol 9 (2) ◽

pp. 74

Author(s):

Eric Hsueh-Chan Lu ◽

Jing-Mei Ciou

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Error Correction ◽

Spatial Information ◽

Rapid Development ◽

Indoor Positioning ◽

Experimental Results ◽

Global Navigation Satellite Systems ◽

Signal Interference ◽

Position Vector

With the rapid development of surveying and spatial information technologies, more and more attention has been given to positioning. In outdoor environments, people can easily obtain positioning services through global navigation satellite systems (GNSS). In indoor environments, the GNSS signal is often lost, while other positioning problems, such as dead reckoning and wireless signals, will face accumulated errors and signal interference. Therefore, this research uses images to realize a positioning service. The main concept of this work is to establish a model for an indoor field image and its coordinate information and to judge its position by image eigenvalue matching. Based on the architecture of PoseNet, the image is input into a 23-layer convolutional neural network according to various sizes to train end-to-end location identification tasks, and the three-dimensional position vector of the camera is regressed. The experimental data are taken from the underground parking lot and the Palace Museum. The preliminary experimental results show that this new method designed by us can effectively improve the accuracy of indoor positioning by about 20% to 30%. In addition, this paper also discusses other architectures, field sizes, camera parameters, and error corrections for this neural network system. The preliminary experimental results show that the angle error correction method designed by us can effectively improve positioning by about 20%.

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Sparse Representation Optimization of Gaussian Mixed Feature of Image Based on Convolution Neural Network

10.21203/rs.3.rs-912092/v1 ◽

2021 ◽

Author(s):

Yuguang Ye

Keyword(s):

Neural Network ◽

Image Processing ◽

Sparse Representation ◽

Rapid Development ◽

Vehicle Detection ◽

Response Speed ◽

Gaussian Mixture ◽

Image Features ◽

Convolution Neural Network ◽

Processing Efficiency

Abstract With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.

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REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

ICTACT Journal on Image and Video Processing ◽

10.21917/ijivp.2017.0218 ◽

2017 ◽

Vol 8 (1) ◽

pp. 1533-1542

Author(s):

S Safinaz ◽

◽

A V Ravi Kumar ◽

Keyword(s):

Neural Network ◽

Real Time ◽

Network Architecture ◽

Convolution Neural Network ◽

Neural Network Architecture

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Application of neural networks in industrial production

10.33920/pro-2-2106-01 ◽

2021 ◽

pp. 10-17

Author(s):

S. S. Yudachev ◽

N. A. Gordienko ◽

F. M. Bosy

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Effective Means ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Graphics Processors ◽

Training Scheme ◽

Network Training ◽

The Neural Network

The article describes an algorithm for the synthesis of neural networks for controlling the gyrostabilizer. The neural network acts as an observer of the state vector. The role of such an observer is to provide feedback to the gyrostabilizer, which is illustrated in the article. Gyrostabilizer is a gyroscopic device designed to stabilize individual objects or devices, as well as to determine the angular deviations of objects. Gyrostabilizer systems will be more widely used, as they provide an effective means of motion control with a number of significant advantages for various designs. The article deals in detail with the issue of specific stage features of classical algorithms: selecting the network architecture, training the neural network, and verifying the results of feedback control. In recent years, neural networks have become an increasingly powerful tool in scientific computing. The universal approximation theorem states that a neural network can be constructed to approximate any given continuous function with the required accuracy. The back propagation algorithm also allows effectively optimizing the parameters when training a neural network. Due to the use of graphics processors, it is possible to perform efficient calculations for scientific and engineering tasks. The article presents the optimal configuration of the neural network, such as the depth of memory, the number of layers and neurons in these layers, as well as the functions of the activation layer. In addition, it provides data on dynamic systems to improve neural network training. An optimal training scheme is also provided.

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Real-Time Video Scaling Based on Convolution Neural Network Architecture

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v7.i2.pp381-394 ◽

2017 ◽

Vol 7 (2) ◽

pp. 381

Author(s):

S Safinaz ◽

AV Ravi kumar

Keyword(s):

Neural Network ◽

High Resolution ◽

Real Time ◽

Network Architecture ◽

High Efficiency ◽

Super Resolution ◽

Reconstruction Error ◽

Convolution Neural Network ◽

Neural Network Architecture ◽

Video Frames

In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network) shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

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