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Skeleton model based behavior recognition for pedestrians and cyclists from vehicle scene camera

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Skeleton model based behavior recognition for pedestrians an...

IEEE Intelligent Vehicles Symposium

作者： Qiwen Deng Renran Tian Yaobin Chen Kang Li Transportation Active Safety Institute and Department of Electrical and Computer Engineering Indiana University Purdue University Indianapolis Department of Industrial and Systems Engineering School of Engineering Rutgers University

ISBN: (纸本)9781538644539

With the significant advances in computer vision research, skeleton model based human pose recognition has become more accurate and time-efficient, although most of the applications are limited in laboratory environment or on surveillance videos. This paper proposes a pose tracking and behavior recognition method fromgehicle scene camera. It will not only detect pedestrians on the road, but also generate their skeleton models describing head, limb, and trunk movements. Based on these more detailed movements of body parts, the proposed method is designed to track poses of pedestrians and cyclists with the potentials to enable automated pedestrian gesture reading and non-verbal interactions between autonomous vehicles and pedestrians. The proposed algorithm has been tested on different databases including TASI 110-car naturalistic driving database and Joint Attention for Autonomous Driving (JAAD) database. Results show that key frames describing different pedestrian and cyclist negotiation gestures are detected from the raw video streams using the proposed method. These results will improve our understanding of pedestrian and cyclist's intentions and can be further used for autonomous vehicle control algorithm development.

关键词： Pose tracking Behavior recognition Pedestrian and cyclists Human-autonomous vehicle interaction

Coordinating vehicle platoons for highway bottleneck decongestion and throughput improvement

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arXiv 2019年

作者： Čičić, Mladen Jin, Li Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Malvinas väg 10 Stockholm10044 Sweden Department of Civil and Urban Engineering C2SMART University Transportation Center New York University Tandon School of Engineering 15 MetroTech Center BrooklynNY11201 USA United States

Truck platooning is a technology that is expected to become widespread in the coming years. Apart from the numerous benefits that it brings, its potential effects on the overall traffic situation needs to be studied further, especially at bottlenecks and ramps. However, assuming we can control the truck platoons from the infrastructure, they can be used as controlled moving bottlenecks, actuating control actions on the rest of the traffic. Thus properly controlled platoons can possibly improve the efficiency and throughput of the whole system. In this paper, we use a multi-class cell transmission model to capture the interaction between truck platoons and background traffic, and propose a corresponding queuing model, which we use for control design. As control inputs, we use platoon speeds, and the number of lanes platoons occupy, and we devise a control strategy for throughput improvement. This control law is applied on a highway section upstream of a bottleneck, with one on- ramp and one off-ramp. By postponing and shaping the inflow to the bottleneck, we are able to keep it in free flow, avoiding traffic breakdown and capacity drop, leading to significant reduction of total time spent of all vehicles. We tested the proposed control in a simulation study and found that by applying, we reduce the median delay of all vehicles by 75:6%. Notably, although they are slowed down while actuating control actions, platoons experience less delay compared to the case without control, since they avoid going through congestion at the bottleneck. Copyright © 2019, The Authors. All rights reserved.

关键词： Queueing theory

Social Energy:Mining Energy From the Society

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 466-482页

作者： Jun Jason Zhang David Wenzhong Gao Yingchen Zhang Xiao Wang Xiangyang Zhao Dongliang Duan Xiaoxiao Dai Jun Hao Fei-Yue Wang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of Denver IEEE National Renewable Energy Laboratory Qingdao Academy of Intelligent Industries(QAII) QingdaoShandongChinaand SKL-MCCSCASIA School of Automation Science and Electrical Engineering Beihang University Department of Electrical and Computer Engineering College of Engineering and Applied ScienceUniversity of Wyoming State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Research Center of Computational Experiments and Parallel Systems National University of Defense Technology

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social *** this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system *** recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and *** this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy *** the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

关键词： ACP distribution locational marginal pricing intelligent operation in power systems parallel control parallel intelligence parallel management social energy socio-technical energy systems

Capsule networks for brain tumor classification based on MRI images and course tumor boundaries

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arXiv 2018年

作者： Afshar, Parnian Plataniotis, Konstantinos N. Mohammadi, Arash Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada Department of Electrical and Computer Engineering University of Toronto TorontoON Canada

According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate. Determining the type of brain tumor has significant impact on the treatment choice and patient's survival. Human-centered diagnosis is typically error-prone and unreliable resulting in a recent surge of interest to automatize this process using convolutional neural networks (CNNs). CNNs, however, fail to fully utilize spatial relations, which is particularly harmful for tumor classification, as the relation between the tumor and its surrounding tissue is a critical indicator of the tumor's type. In our recent work, we have incorporated newly developed CapsNets to overcome this shortcoming. CapsNets are, however, highly sensitive to the miscellaneous image background. The paper addresses this gap. The main contribution is to equip CapsNet with access to the tumor surrounding tissues, without distracting it from the main target. A modified CapsNet architecture is, therefore, proposed for brain tumor classification, which takes the tumor coarse boundaries as extra inputs within its pipeline to increase the CapsNet's focus. The proposed approach noticeably outperforms its counterparts. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

BRAIN TUMOR TYPE CLASSIFICATION VIA CAPSULE NETWORKS

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BRAIN TUMOR TYPE CLASSIFICATION VIA CAPSULE NETWORKS

IEEE International Conference on Image Processing

作者： Parnian Afshar Arash Mohammadi Konstantinos N. Plataniotis Concordia Institute for Information Systems Engineering Concordia University Montreal QC Canada Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient's response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neural Networks (CNNs) for the problem of brain tumor type classification. However, CNNs typically require large amount of training data and can not properly handle input transformations. Capsule networks (referred to as CapsNets) are brand new machine learning architectures proposed very recently to overcome these shortcomings of CNNs, and posed to revolutionize deep learning solutions. Of particular interest to this work is that Capsule networks are robust to rotation and affine transformation, and require far less training data, which is the case for processing medical image datasets including brain Magnetic Resonance Imaging (MRI) images. In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the accuracy of the classification problem at hand;(ii) Investigate the over-fitting problem of CapsNets based on a real set of MRI images;(iii) Explore whether or not CapsNets are capable of providing better fit for the whole brain images or just the segmented tumor, and;(iv) Develop a visualization paradigm for the output of the CapsNet to better explain the learned features. Our results show that the proposed approach can successfully overcome CNNs for the brain tumor classification problem.

关键词： Tumors Feature extraction Magnetic resonance imaging Neurons computer architecture Cancer Training data

Brain tumor type classification via capsule networks

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arXiv 2018年

作者： Afshar, Parnian Mohammadi, Arash Plataniotis, Konstantinos N. Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada Department of Electrical and Computer Engineering University of Toronto TorontoON Canada

关键词： Brain

Improved explainability of capsule networks: Relevance path by agreement

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arXiv 2018年

作者： Shahroudnejad, Atefeh Mohammadi, Arash Plataniotis, Konstantinos N. Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada Department of Electrical and Computer Engineering University of Toronto TorontoON Canada

Recent advancements in signal processing and machine learning domains have resulted in an extensive surge of interest in deep learning models due to their unprecedented performance and high accuracy for different and challenging problems of significant engineering importance. However, when such deep learning architectures are utilized for making critical decisions such as the ones that involve human lives (e.g., in medical applications), it is of paramount importance to understand, trust, and in one word "explain" the rational behind deep models' decisions. Currently, deep learning models are typically considered as black-box systems, which do not provide any clue on their internal processing actions. Although some recent efforts have been initiated to explain behavior and decisions of deep networks, explainable artificial intelligence (XAI) domain is still in its infancy. In this regard, we consider capsule networks (referred to as CapsNets), which are novel deep structures;recently proposed as an alternative counterpart to convolutional neural networks (CNNs), and posed to change the future of machine intelligence. In this paper, we investigate and analyze structures and behaviors of the CapsNets and illustrate potential explainability properties of such networks. Furthermore, we show possibility of transforming deep learning architectures in to transparent networks via incorporation of capsules in different layers instead of convolution layers of the CNNs. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

CARISI: CONVOLUTIONAL AUTOENCODER-BASED INTER-SLICE INTERPOLATION OF BRAIN TUMOR VOLUMETRIC IMAGES

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CARISI: CONVOLUTIONAL AUTOENCODER-BASED INTER-SLICE INTERPOL...

IEEE International Conference on Image Processing

作者： Parnian Afshar Atefeh Shahroudnejad Arash Mohammadi Konstantinos N. Plataniotis Concordia Institute for Information Systems Engineering Concordia University Montreal QC Canada Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

The paper is motivated by the fact that brain cancer is one of the deadliest cancers and its detection in early stages is of paramount importance. In this regard, tumor 3D shape reconstruction from magnetic resonance (MR) or computed tomography (CT) scans provides critical information, which can not be interpreted from 2D images. However, CT and MR images have low resolution in z direction compared to their resolution in x and y directions, therefore, 3D reconstructed shapes are of low quality. In this paper, we propose to use convolutional auto-encoders (CAEs) to address this drawback, and develop a convolutional autoencoder-based inter-slice interpolation (CARISI) framework. Although deep nets have been used very recently for brain tumor segmentation, to the best of our knowledge, this is the first attempt to use CAEs for 3D reconstruction of brain tumor. The proposed CARISI framework consists of several encoding and decoding components, which can handle rapid changes in tumor shape without the need for supervision of an expert. Our experiments based on a real data-set consisting of 3064 segmented brain tumor images indicate that the proposed CARISI framework outperforms its counterpart and has the potential to significantly improve the overall quality of the reconstructed shapes.

关键词： Tumors Three-dimensional displays Interpolation Image reconstruction Shape Computed tomography Convolution

AIM 2019 challenge on real-world image super-resolution: Methods and results

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arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power