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Automated Real-Time At-Scene Reporting System

Automated Real-Time At-Scene Reporting System

引用

International conference on Information and Communication Technology for the Muslim World (ICT4M)

作者： Olowolayemo, Akeem Qing, Pearly Oh Bei Univ Malaysia Sarawak Fac Cognit Sci & Human Dev Dept Cognit Sci Kota Samarahan Sarawak Malaysia

ISBN: (纸本)9781538675250

This paper proposes a framework for real-time tracking of objects in real scene using computer vision technique implemented on a mobile application for news reporting. This automated reporting system is intended to complement news reporters in reporting real-time scenario especially in dangerous environment such as disaster situation, fire, explosion or war zones. The system comprises assembly of some functionalities and algorithms to successfully build and run function by converting scene images into paragraphing and voice output to describe the scenarios in real-time. A variety of functions such as object motion detection, scene recognition, emotional recognition and distance detection are presented so that paragraphing and voice generation can be more accurate and close in meaning to real-time human reporting situations. Since the proposed system can facilitate news reporting in real turbulent situations, the burden of news reporting can be reduced under such dangerous circumstances. This work briefly reviews the rudimentary concepts of image processing and computer vision that serve as the components of the proposed real-time automated reporting system and describes how these various compelling applications are coupled and work together. The work also outlines the choice of design for effectiveness and efficiency of such reporting systems.

关键词： Artificial intelligence Automated reporter Scene Recognition Motion detection Emotion detection Distance detection image paragraphing Voice Output

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Self-Supervised Learning of the Drivable Area for Autonomous Vehicles

Self-Supervised Learning of the Drivable Area for Autonomous...

引用

25th IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Mayr, Jakob Unger, Christian Tombari, Federico BMW Grp D-80788 Munich Germany Tech Univ Munich Chair Comp Aided Med Procedures Boltzmannstr 3 D-85748 Garching Germany

ISBN: (纸本)9781538680940

We propose a new approach for generating training data for the task of drivable area segmentation with deep neural networks (DNN). The impressive progress of deep learning in recent years demonstrated a superior performance of DNNs over traditional machine learning and deterministic algorithms for various tasks. Nevertheless, the acquisition of large-scale datasets with associated ground truth labels still poses an expensive and labor-intensive problem. We contribute to the solution of this problem for the task of road segmentation by proposing an automatic labeling pipeline which leverages a deterministic stereo-based approach for ground plane detection to create large datasets suitable for training neural networks. Based on the popular Cityscapes [1] and KITTI dataset [2] and two off-the-shelf DNNs for semantic segmentation, we show that we can achieve good segmentation results on monocular images, which substantially exceed the performance of the algorithm employed for automatic labeling without the need of any manual annotation.

关键词： image segmentation Training data Labeling Generators Histograms Training Cameras

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The Application of Neural Networks for Facial Landmarking on Mobile Devices 13

The Application of Neural Networks for Facial Landmarking on...

引用

13th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / International conference on Computer Vision Theory and Applications (VISAPP)

作者： Kendrick, Connah Tan, Kevin Walker, Kevin Yap, Moi Hoon Manchester Metropolitan Univ Sch Comp Math & Digital Technol John Dalton Bldg Manchester Lancs England Image Metr Ltd City TowerPiccadilly Plaza Manchester Lancs England

ISBN: (纸本)9789897583063

Many modern mobile applications incorporate face detection and landmarking into their systems, such as Snapchat, beauty filters and camera auto-focusing systems, where they implement regression based machine learning algorithms for accurate face landmark detection, allowing the manipulation of facial appearance. The mobile applications that incorporate machine learning have to overcome issues such as lighting, occlusion, camera quality and false detections. A solution could be provided through the resurgence of deep learning with neural networks, as they are showing significant improvements in accuracy and reliability in comparison to the state-of-the-art machine learning. Here, we demonstrate the process by using trained networks on mobile devices and review its effectiveness. We also compare the effects of employing max-pooling layers, as an efficient method to reduce the required processing power. We compared network with 3 different amounts of max-pooling layer and ported one to the mobile device, the other two could not be ported due to memory restrictions. We will be releasing all code to build, train and use the model in a mobile application. The results show that despite the limited processing capability of mobile devices, neural networks can be used for difficult challenges while still working in real-time. We show a network running on a mobile device on a live data stream and give a recommendation on the structure of the network.

关键词： Facial Landmarking Android Deep Learning

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Abnormal behaviour detection at traffic junctions using Lucas Kanade and Harris Corner detector 4

Abnormal behaviour detection at traffic junctions using Luca...

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4th IEEE International conference on Recent Advances in Information Technology, RAIT 2018

作者： Jain, Charvi Gautam, Diwakar Department of Computer Science and Engineering Indian Institute of Information Technology UnaHimachal Pradesh India Department of Computer Science and Engineering Sharda University Knowledge Park III Greater NoidaUttar Pradesh India

ISBN: (纸本)9781538630396

image processing has played a vital role in every aspect of human life. Video surveillance has reached a major out through by the application of advanced image processing and frame modeling techniques. Video surveillance is the most recent issue regarding community security and welfare. Detection of behavior of moving objects in video streams is a vital aspect. In this article, automatic real-time object's behavior detection is implemented using Lucas Kanade and Harris Corner based approach. The velocity of moving element is reported and their association with anomalous activity is also an inseparable part of this art. This work can be used to develop a surveillance system of static camera and robotic automation visual systems. Whenever a new object comes in the camera frame, the system uses the concepts of frame based processing using Lucas Kanade approach incorporated with Harris Corner Detector. In this work, an investigation is carried out to define the optimum value of velocity parameters for declaring a behavior as anomalous. Moreover, it is analyzed that proposed algorithm for video behaviour detection at traffic points perform well but its accuracy can be further enhanced using three dimensional imaging and mapping of moving elements in a hyperspace. © 2018 IEEE.

关键词： Object detection

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BirdNet: A 3D Object Detection Framework from LiDAR Information 21

BirdNet: A 3D Object Detection Framework from LiDAR Informat...

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21st IEEE International conference on Intelligent Transportation systems, ITSC 2018

作者： Beltrán, Jorge Guindel, Carlos Moreno, Francisco Miguel Cruzado, Daniel García, Fernando De La Escalera, Arturo Research Group Universidad Carlos III de Madrid Leganés Madrid Spain

ISBN: (纸本)9781728103235

Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles;however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision. We present a LiDAR-based 3D object detection pipeline entailing three stages. First, laser information is projected into a novel cell encoding for bird's eye view projection. Later, both object location on the plane and its heading are estimated through a convolutional neural network originally designed for image processing. Finally, 3D oriented detections are computed in a post-processing phase. Experiments on KITTI dataset show that the proposed framework achieves state-of-the-art results among comparable methods. Further tests with different LiDAR sensors in real scenarios assess the multi-device capabilities of the approach. © 2018 IEEE.

关键词： Object detection

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Smart Annotation Tool for Multi-sensor Gait-based Daily Activity Data

Smart Annotation Tool for Multi-sensor Gait-based Daily Acti...

引用

IEEE International conference on Pervasive Computing and Communications (PerCom)

作者： Martindale, Christine F. Roth, Nils Hannink, Julius Sprager, Sebastijan Eskofier, Bjoern M. Friedrich Alexander Univ Erlangen Nurnberg Machine Learning & Data Analyt Lab Comp Sci Dept Erlangen Germany Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia

ISBN: (纸本)9781538632277

The monitoring of patients within a natural, home environment is important in order to close knowledge gaps in the treatment and care of neurodegenerative diseases, such as quantifying the daily fluctuation of Parkinson's patients' symptoms. The combination of machine learning algorithms and wearable sensors for gait analysis is becoming capable of achieving this. However, these algorithms require large, labelled, realistic datasets for training. Most systems used as a ground truth for labelling are restricted to the laboratory environment, as well as being large and expensive. We propose a study design for a realistic activity monitoring dataset, collected with inertial measurement units, pressure insoles and cameras. It is not restricted by a fixed location or capture volume and still enables the labelling of gait phases or, where non-gait movement such as jumping occur: on-the-ground, off-the-ground phases. Additionally, this paper proposes a smart annotation tool which reduces annotation cost by more than 80%. This smart annotation is based on edge detection within the pressure sensor signal. The tool also enables annotators to perform assisted correction of these labels in a post-processing step. This system enables the collection and labelling of large, fairly realistic datasets where 93% of the automatically generated labels are correct and only an additional 10% need to be inserted manually. Our tool and protocol, as a whole, will be useful for efficiently collecting the large datasets needed for training and validation of algorithms capable of cyclic human motion analysis in natural environments.

关键词： image edge detection Tools Cameras Labeling Pressure sensors Intelligent sensors

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An Ensemble Model for Error Modeling with Pseudoinverse Learning Algorithm

An Ensemble Model for Error Modeling with Pseudoinverse Lear...

引用

IEEE International conference on systems, Man and Cybernetics

作者： Sibo Feng Xiaodan Deng Ping Guo Bo Zhao Qian Yin Hongfeng Wang Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Information Management Dezhou University Shandong China

In Bayesian theory, the maximum posterior estimator uses prior information to estimate the noise in the machine learning model by adding the regularization term. The regularization terms L 1 and L 2 correspond to Laplacian prior and Guassian prior, respectively. In existing deep learning models, in order to use the gradient descent optimization algorithm and achieve good results, most models take L 2 regularization as the regularization term of the network model to fit the complex Guassian noise. However in practice, the Laplace noise and the Guassian noise are both considered as data noise. For multi-layer perceptrons, the difficulty caused by adding L 1 and L 2 into the optimization function of the network is solved by proposing an ensemble model for error modeling through adopting the divide and conquer strategy. First, several base learners are trained to fit different noise distributions of data, then the final results can be obtained by taking the results of each base leaner as new data to train a meta leaner, and get the final results. Among them, coordinate regression method is used to solve L 1 loss, while the pseudo-inverse learning algorithm is employed to solve L 2 loss. Both methods are nongradient optimization algorithms. The comparison results of the model on several data sets show that the proposed ensemble model achieves better performance.

关键词： Training Optimization Data models Neural networks Computational modeling Matrix decomposition Estimation

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Sensitivity Estimation and image Reconstruction for Sparse PET with Deep Learning

Sensitivity Estimation and Image Reconstruction for Sparse P...

引用

IEEE Nuclear Science Symposium and Medical Imaging conference (NSS/MIC) / 25th International Symposium on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Feng, Tao Wang, Jizhe Li, Hongdi UIH Amer Inc Houston TX 77054 USA

ISBN: (纸本)9781538684948

The use of a sparse crystal setting would reduce the cost of the PET scanner and has advantages such as less RF shielding in PET/MR. It also allows a longer axial field of view (FOV) using the same crystal volume. In this paper, the sensitivities of the coincidence events of PET systems with the sparse crystal configuration, thin crystal setting, and the conventional design using a fixed total crystal volume were analytically estimated. The sinograms of a sparse system (with 50% crystal removed and fixed axial FOV) were simulated using patient data. Reconstruction algorithms were developed by modeling the effects of reduced crystals in the system matrix. A convolutional neural network (CNN) based noise reduction approach was used for post-processing. A total of 14 patient data were included and were truncated to 3 minutes scan for consistency. Leave-one-out cross- validation was used for evaluation purpose. A patch based data input/output was used for model training to increase the number of training samples. images reconstructed using OSEM followed by Gaussian denoising was also used as a comparison. The percentage summed square difference (SSD) between images of sparse crystal configuration and non-sparse systems were used for quantitative evaluation. When using the same total volume of crystals, the difference of sensitivity at the center of FOV was within 10% among three different settings, with the rank from highest to lowest being the thin detector, sparse detector, and conventional detector. When using the same axial FOV, reconstructed images of the sparse crystal configuration showed increased noise due to reduced sensitivity. The percentage SSD for image processed with the Gaussian filter was 30% on average and was reduced to 16% with CNN on average. The results show with the same amount of crystal, the use of sparse crystal configuration provides a slightly larger sensitivity and much larger axial FOV. CNN processed images was able to partially recover los

关键词： Crystals Sensitivity image reconstruction Detectors Noise reduction Training image quality

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Driver safety approach using efficient image processing algorithms for driver distraction detection and alerting 6th

Driver safety approach using efficient image processing algo...

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6th International conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA-2017

作者： Wathiq, Omar Ambudkar, Bhavna D. E&TC Dr. D.Y. Patil Institute of Engineering & Technology Pimpri Pune411018 India

ISBN: (纸本)9789811075650

Currently, due to different reasons, the road accidents are increasing. Road accidents are prone to number human deaths. There are different reasons which lead to road accidents, but drivers fatigue or distraction is main threat in major accidental cases. Therefore, recently various methods are explained by many authors for untimely identification of driver sleepiness in the manner of prohibiting mischance on road. In this paper, we are presenting the novel approach called hybrid method in which automatic care of driver safety and hospitality management services. Our approach aims at determining first if a driver is distracted or not based yawing, eye position, head position, mouth position etc., second if driver is detected as distracted instance alarming will perform on both driver side and near hospital services in order to be available in case of accident happen. Based on computer vision techniques, we propose four different modules for features extraction, focusing on arm position, face orientation, facial expression and eye behaviour, and then, the outputs of all these phases combined together and feed to the classifier feed-forward neural network (FFNN) for alarming the distraction detection and type of distraction. The outcome of this paper is efficient driver safety approach by considering the RGB-D sensor and image processing algorithms. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Feature extraction

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Fully automated CADx for early breast cancer detection using image processing and machine learning 30

Fully automated CADx for early breast cancer detection using...

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30th International conference on Microelectronics, ICM 2018

作者： Gamil, Monica Ezzat Mohamed Fouad, Mariam Abd El Ghany, Mohamed A. Hoffinan, Klaus Electronics Engineering Dept German University in Cairo Cairo Egypt Electronics Department German University Cairo Egypt Integrated Electronic Systems Lab TU Darmstadt Germany

ISBN: (纸本)9781538681671

Breast cancer accounts for 16% of all cancers among females. Current early detection methods are expensive or computationally complex and thus unsuitable for developing countries. For this reason, a real-time fully automated Computer Aided Diagnosis system for Breast Cancer early detection from Ultrasound images is built in this paper. The proposed and implemented design comprises into its modules state of the art techniques and methods. The implemented design includes preprocessing/filtering of the input ultrasound image, segmentation of the region of interest from the background image and feature set calculation/extraction. Machine learning algorithms were implemented for classification of the tumour. Successful implementation with satisfactory run time is achieved with a final accuracy improved by 10% from previous work using the same set of features. Additional evaluation metrics like precision-recall plots and confusion matrices were also used to test and evaluate the system overall balanced performance. © 2018 IEEE.

关键词： Machine learning

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