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Crowd R-CNN: An Object Detection Model Utilizing Crowdsourced Labels 2019

Crowd R-CNN: An Object Detection Model Utilizing Crowdsource...

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Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Yucheng Hu Meina Song School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450376259

Accuracy of object detection has increased significantly in recent years because of the rapid development of deep learning techniques. Nevertheless, its applications in many fields are still limited, mainly due to the lack of large datasets, especially datasets with accurate annotations. Crowdsourcing provides a promising approach to tackle the problem mentioned above because of their "divide and conquer" nature. Nonetheless, existing crowdsourced techniques, e.g., Amazon Mechanical Turk (MTurk), often fail to guarantee the quality of the annotations. In this paper, we propose a novel probabilistic scheme based on crowdsourcing for ground truth inference. As a representative of object detection, we choose Faster R-CNN as the base framework. We name our scheme Crowd R-CNN. We propose an aggregation approach to aggregate annotations from multiple annotators, which allows to convert anchor labels and annotated labels with each other and train the network end-to-end using backpropagation. To improve accuracy, Crowd R-CNN takes into consideration the multi-dimensional measure of the annotatore' ability and updates these parameters during training. Experimental results demonstrate that Crowd R-CNN can deal with noisy crowdsourced data effectively. Crowd R-CNN is able to achieve comparable results to the baseline with ground truth annotations and is the first algorithm to solve the problem of how to train deep object detection model utilizing crowdsourced labels.

关键词： Deep Learning Object Detection Crowdsourcing Ground Truth Inference Aggregation

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A New Method to Detect Moving Targets

A New Method to Detect Moving Targets

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2018 3rd international conference on Education, Management and Systems Engineering(EMSE 2018)

作者： Yang-ming HE Ya-fen WU School of Computer Jiangxi University of Traditional Chinese Medicine Office of Academic Research Jiangxi University of Traditional Chinese Medicine

In the moving target detection, there are a lot of methods. But the effects of those algorithms are limited, especially in complex environment. This paper putts forward a new method to detect moving targets, which uses virtual binocular disparity. It is called virtual because the environment uses one camera but the theory is about two cameras. Virtual binocular disparity algorithm regards moving targets as static targets under two cameras. It uses two neighboring images, the current frame image and the next frame image. The algorithm looks the two images as two static images under two cameras, then uses stereo match theory to calculate the disparity of the neighboring images. Because the background of the two images is the same, the disparity of their background is about zero. And the disparity of moving targets is positive or negative. The sign of disparity means the direction of targets moving. So we can use the sign of disparity to distinguish the targets moving in different directions. The direction detected out will be helpful for subsequent work, for example, targets tracking and counting etc. This algorithm fully uses the continuity property of moving targets, so it can have good effect. In the end of the paper, experiments are used to prove the algorithm.

关键词： Virtual binocular disparity Stereo match Moving targets image processing

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A Novel Simulation System Oriented Description Language of Worm Propagation

A Novel Simulation System Oriented Description Language of W...

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2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： Zhigang Zhang Tianbo Wang Chunhe Xia Honglin Ren Beijing Key Laboratory of Network Technology Beihang University School of Computer Science and Engineering Beihang University School of Cyber Science and Technology Beihang University School of Computer Science and Information Technology Guangxi Normal University

Software simulation is one of the most common methods to study worm propagation. However, simulation process and software interface, which lack unified description, cause inefficiencies of worm propagation simulation. In order to achieve a unified description for worm propagation simulation process, we propose a Worm Propagation Simulation Description Language(WPSDL), which characterizes the software interfaces and parameters of each module in simulation process in a unified way. Meanwhile, we develop a simulation system of worm propagation oriented our presented language. In this system, we use related commands of the description language as interactive instructions, and then implement worm propagation simulation for different types of network worms. In addition, we conduct a series of experiments to analyse the impacts of worm propagation mechanism and network topology structure. The experiments show that increasing infection ability and scanning rate can improve the propagation scale significantly, and the propagation delay can influence the outbreak time and explosion intensity of worm propagation. The experiments not only provide support for the study of worm propagation mechanisms, but also verify the practicability of the description language and the simulation system.

关键词： simulation A Novel Simulation System Oriented Description Language of Worm Propagation Simulation

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Expression of Concern for: A scheme of long short-term memory (LSTM) -CNN features extraction and spatial relationship to anonymize image and video for Privacy-Preserving Human Activity Recognition of multiple sequences of data

Expression of Concern for: A scheme of long short-term memor...

引用

international Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Kambala Vijaya Kumar Yalanati Ayyappa B Vinay Kumar Harikiran Jonnadula Yamini Tondepu Nusumu Sree Vidya Department of CSE P.V. P Siddhartha Institute of Technology-Vijayawada Andhra Pradesh India School of CSE VIT-AP University Amaravathi Andhra Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350320749

ISBN: (纸本)9798350320756

Safeguarding security from stowed away pictures and recorded recordings is a significant social issue. To store information related picture or video data the arrangement of human movement can be recognized for examining the different issues of exercises. And yet guarantee that it doesn’t record the significant video or catch the activities of human it is abusing the security of human. This article presents a crucial way to deal with tackling such clashing undertakings: acknowledgment of human movement while utilizing just mysterious low-goal recordings (Example: lSx16). We are presenting the Deep learning, CNN, computer vision OpenCV worldview, the calculation making sense of the insignificant picture transformation and to make a few low-goals of profoundly goal informative recordings from video. Our ideas is concentrating on different kinds of sub-pixel changes that are streamlined for arranging exercises, which permits the study hall expert to utilize existing high-goal recordings by bringing a few LR preparing recordings adjusted for this issue. The commonsense result results are affirmed that the OpenCV in PC models can profit from movement acknowledgment from very human action recordings. In this paper we are seeing as the negligible of human exercises from the pictures or recordings taken from the different public where the high goal can be distinguished and getting the exceptionally low goal and protection can be stowed away from such pictures or recordings.

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A Machine Learning Distracted Driving Prediction Model 2019

A Machine Learning Distracted Driving Prediction Model

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Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Samira Ahangari Mansoureh Jeihani Abdollah Dehzangi Department of Transportation and Infrastructure Studies Morgan State University USA Department of Computer Science Morgan State University USA

ISBN: (纸本)9781450376259

Distracted driving is known to be one of the core contributors to crashes in the U.S., accounting for about 40% of all crashes. Drivers' situational awareness, decision-making, and driving performance are impaired due to temporarily diverting their attention from the primary task of driving to other tasks not related to driving. Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving *** this study, we use a Bayesian Network classifier as a powerful machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of hand-held calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performances such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were *** we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. We also achieved 62.6% true positive rate, which demonstrates the ability of our model to correctly predict distractions.

关键词： Data Mining Driving Simulator Machine Learning Distracted Driving Bayesian Network

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The Recognition of Maize seeds Based on Multi-scale Feature Fusion and Extreme Learning Machine

The Recognition of Maize seeds Based on Multi-scale Feature ...

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2015 3rd international conference on Mechanical Engineering and Intelligent Systems(ICMEIS 2015)

作者： Mingzhi Du Xiao Ke Mingke Zhou College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University)

In recognizing traditional crops seeds like maize seeds, we usually use electrophoresis assay method, fluorescence scanning method and chemical assay method. These methods are destructive methods. They take a long time to detect and are demanding of professional background knowledge and hardware conditions etc. What's more, these methods, based on BP neural network and support vector machine(SVM)while taking a long time to detect are less accurate in process of classification. In this paper, based on the computer vision technology, we proposed a new method for the classification of maize seeds, a method based on multi-scale feature fusion and extreme learning machine. First, we extract the multi-scale fusion feature of maize seeds. Second, based on extreme learning machine, we construct the classifier model of maize seed. Third, because of the window of image in the case of multi-scale detection has the problem of capturing the same object seed with many overlapping windows, we put forward a kind of window fusion algorithm to solve it. The simulation results show that: The method is able to identify the maize seeds accurately. Using this method the accuracy of classification of maize seeds can reached 97.66% and the error rate is less than 0.1%. Compared with the traditional methods, the method we proposed can improve the speed of detection and the accuracy of classification, and has no strict hardware requirements.

关键词： extreme learning machine feature fusion object recognition

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Wavelet Transform and Artificial Neural Network Based Spectrum Sensing in Cognitive Radio

Wavelet Transform and Artificial Neural Network Based Spectr...

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2018 3rd international conference on Communication,image and Signal processing（CCISP 2018）

作者： DU Ruiyan GAO Fan WANG Xianchao ZHANG Dandan LIU Fulai Engineer Optimization & Smart Antenna Institute Northeastern University at Qinhuangdao School of Computer Science and Engineering Northeastern University

With the progress and upgrade of wireless telecommunication technologies, the lack of spectrum resources is pretty serious. As a method to improve spectrum utilization, cognitive radio has received widespread attention. The technology of spectrum sensing, one of the most significant parts of cognitive radio, has important research value. The existing spectrum sensing technology is difficult to detect the main user signal at a lower signal-to-noise-ratio(SNR).To solve this problem, wavelet transform and artificial neural network based spectrum sensing in cognitive radio is put forward. The spectrum detection perceptual classifier is designed through the wavelet transform, cyclostationary feature and the artificial neural network. Firstly, the wavelet threshold method is used to process the received signal. The cyclic characteristic value of the processed signal is calculated. Secondly, the eigenvalues are learned and stored through the artificial neural network. Finally, the test data is brought into the trained network for classification. Verifying by simulation experiments, the proposed algorithm has better accuracy and effectiveness than the original method in the low SNR environment.

关键词： SNR Wavelet Transform and Artificial Neural Network Based Spectrum Sensing in Cognitive Radio Radio

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A Survey Paper on Plant Disease Identification Using Machine Learning Approach

A Survey Paper on Plant Disease Identification Using Machine...

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international conference on Communication and Electronics Systems

作者： Jyoti Shirahatti Rutuja Patil Pooja Akulwar Computer Science & Engineering Department Sanjay Ghodawat Group of Institutions Computer Science & Engineering Department Sanjay Ghodawat University

ISBN: (纸本)9781538647660

Agriculture plays an important role in farmer's life. Sometimes manual identification of disease is time consuming and need of labor is more. One of the most important facts that reduce the growth of plants is disease attack. Overall study about agriculture shows that quality and quantity of agricultural products may be reduced due to various factors of plant diseases. These diseases can be more easily identified by using machine learning approach as compared to manual method. Hence machine learning method can be used to identify the affected leaf images. images captured by camera will be processed using different image processing technique. These techniques will help in identifying plant diseases thereby increasing the yield of plants. This survey paper describes plant disease identification using Machine Learning Approach and study in detail about various techniques for disease identification and classification is also done.

关键词： Feature extraction image processing techniques SVM PNN ANN and GA

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A Novel Method for Extending V2V System 2019

A Novel Method for Extending V2V System

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Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Qiang Zheng Jian Wang College of Computer Science and Technology Jilin University Jilin China

ISBN: (纸本)9781450376259

Autonomous vehicles requires sufficient perception of the surrounding environment to make proper driving behavior. Vehicle-to-vehicle (V2V) is a technology that allow vehicles exchange location information (i.e. velocity, position) which can improve the perception capabilities of traditional on-board sensors. However, there are still obstacles preventing the roll-out of the V2X technology, mainly the fact that, unless almost the totality of the existing vehicles adopt it, its effectiveness is rather limited. We can't guarantee that all vehicles are V2V vehicles in real environment due to many reasons. In the traditional V2V system, only V2V vehicle have the ability to broadcast their own location information, but non-V2V vehicle can't. But, the situation is somewhat different in our V2V system. Although, non-V2V vehicles don't have the ability to broadcast their own location information, we can let V2V vehicle detect the location information of non-V2V vehicle and broadcast them out. Therefore, we can think that the non-V2V vehicle can also have the ability to broadcast its own location information in our V2V system. In this way, we extend the ability of traditional V2V system to a certain extent. The proposed method is validated under real-world conditions in urban area.

关键词： Intelligent Vehicles V2V Communications Non-V2V Vehicle Detection License Plate Number Recognition Extend V2V System

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Key Points for Feature Extraction Technology of image Targets

Key Points for Feature Extraction Technology of Image Target...

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2018 3rd international conference on Advances in Materials,Mechatronics and Civil Engineering(ICAMMCE 2018)

作者： JianQiang Zhong High-tech Institute

With the progress of human society and the continuous development of science and technology, the research of computer vision in China has made continuous progress and achieved certain achievements. The image target feature extraction technology is a better performance in this field, which can extract the features of the image by two aspects of the overall and local features to ensure the authenticity of the target extraction. In this paper, through expounding various kinds of representation methods of the whole and local extraction feature technology, the field and level of the application of these methods are discussed objectively, and the trend and difficulties in the development of feature extraction technology are analyzed in order to bring a limited reference for the readers.

关键词： image target Feature extraction technology

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