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International Conference on Pattern Recognition

作者： Ge Pan Han Zhang Fan Yu Yonghong Song Yuanlin Zhang Han Yuan School of Software Engineering Xi'an Jiaotong University Xi'an China Distributed and parallel software lab Huawei Technologies Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

For the action localization task, pre-defined action anchors are the cornerstone of mainstream techniques. State-of-the-art models mostly rely on a dense segmenting scheme, where anchors are sampled uniformly over the temporal domain with a predefined set of scales. However, it is not sufficient because action duration varies greatly. Therefore, it is necessary for the anchors or proposals to have a variable receptive field. In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features. In this part, we employ a stronger backbone I3D with SPN (Segment Pyramid Network) instead of C3D to obtain multi-scale features; 2) a simple but useful feature fusion module named LFE (Local Feature Extraction). Compared with the fully connected layer and global average pooling, our LFE model is more advantageous for network to fit and fuse features. 3) a new feature segment aligning method called TPGC (Two Pathway Graph Convolution), which allows one proposal to leverage semantic features of adjacent proposals to update its content and make sure the proposals have a variable receptive field. YOLA add only a small overhead to the baseline network, and is easy to train in an end-to-end manner, running at a speed of 1097 fps. YOLA achieves a mAP of 58.3%, outperforming all existing models including both RGB-based and two stream on THUMOS'14, and achieves competitive results on ActivityNet 1.3.

关键词： Location awareness Fuses Convolution Semantics Feature extraction Pattern recognition Proposals

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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How to Train Your Deep Multi-Object Tracker

How to Train Your Deep Multi-Object Tracker

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yihong Xu Aljoŝa ŝep Yutong Ban Radu Horaud Laura Leal-Taixé Xavier Alameda-Pineda Inria LJK Univ. Grenoble Alpes France Technical University of Munich Munich Germany Distributed Robotics Lab CSAIL MIT USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only certain sub-modules using loss functions that often do not correlate with established tracking evaluation measures such as Multi-Object Tracking Accuracy (MOTA) and Precision (MOTP). As these measures are not differentiable, the choice of appropriate loss functions for end-to-end training of multi-object tracking methods is still an open research problem. In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers. As a key ingredient, we propose a Deep Hungarian Net (DHN) module that approximates the Hungarian matching algorithm. DHN allows estimating the correspondence between object tracks and ground truth objects to compute differentiable proxies of MOTA and MOTP, which are in turn used to optimize deep trackers directly. We experimentally demonstrate that the proposed differentiable framework improves the performance of existing multi-object trackers, and we establish a new state of the art on the MOTChallenge benchmark. Our code is publicly available from https://***/yihongXU/deepMOT.

关键词： Loss measurement Training Standards Target tracking Optimization Neural networks

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How to train your deep multi-object tracker

arXiv

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arXiv 2019年

作者： Xu, Yihong Osep, Aljosa Ban, Yutong Horaud, Radu Leal-Taixé, Laura Alameda-Pineda, Xavier Inria LJK Univ. Grenoble Alpes France Technical University of Munich Germany Distributed Robotics Lab CSAIL MIT United States

关键词： Deep learning

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Natural Criteria for Comparison of Pedestrian Flow Forecasting Models

Natural Criteria for Comparison of Pedestrian Flow Forecasti...

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tomáš Vintr Zhi Yan Kerem Eyisoy Filip Kubiš Jan Blaha Jiří Ulrich Chittaranjan S. Swaminathan Sergi Molina Tomasz P. Kucner Martin Magnusson Gregorz Cielniak Jan Faigl Tom Duckett Achim J. Lilienthal Tomáš Krajník Artificial Intelligence Center Czech Technical University Distributed Artificial Intelligence and Knowledge Laboratory (CIAD) University of Technology of Belfort-Montbeliard (UTBM) France Faculty of Engineering Marmara University Turkey AASS Mobile Robotics and Olfaction Lab Orebro University Sweden Lincoln Centre for Autonomous Systems (L-CAS) University of Lincoln UK

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-the-art pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times.

关键词： Navigation Predictive models Benchmark testing Trajectory Planning Safety Robots

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Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection 14th

Plug-and-Play Supervisory Control Using Muscle and Brain Sig...

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14th robotics: Science and Systems, RSS 2018

作者： Del Preto, Joseph Salazar-Gomez, Andres F. Gil, Stephanie Hasani, Ramin M. Guenther, Frank H. Rus, Daniela MIT Distributed Robotics Lab CambridgeMA02139 United States Vienna University of Technology Cyber-Physical Systems Group Austria Boston University Guenther Lab BostonMA02215 United States

ISBN: (纸本)9780992374747

Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rolling-window classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor’s brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows "plug-and-play" operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task. © 2018, MIT Press Journals. All rights reserved.

关键词： Safety engineering

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Time-varying Pedestrian Flow Models for Service Robots

Time-varying Pedestrian Flow Models for Service Robots

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European Conference on Mobile Robots (ECMR)

作者： Tomáš Vintr Sergi Molina Ransalu Senanayake George Broughton Zhi Yan Jiří Ulrich Tomasz Piotr Kucner Chittaranjan Srinivas Swaminathan Filip Majer Mária Stachová Achim J. Lilienthal Tomáš Krajník Artificial Intelligence Center Czech Technical University Lincoln Centre for Autonomous Systems (L-CAS) University of Lincoln UK Stanford University Distributed Artificial Intelligence and Knowledge Laboratory (CIAD) University of Technology of Belfort-Montbéliard (UTBM) France AASS Mobile Robotics and Olfaction Lab Örebro University Sweden University of Matej Bel in Banska Bystrica Slovakia

We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

关键词： Navigation Hidden Markov models Predictive models Trajectory Legged locomotion

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Correcting robot mistakes in real time using EEG signals

Correcting robot mistakes in real time using EEG signals

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Salazar-Gomez, Andres F. Delpreto, Joseph Gil, Stephanie Guenther, Frank H. Rus, Daniela Boston University Guenther Lab BostonMA02215 United States MIT Distributed Robotics Lab CambridgeMA02139 United States

ISBN: (纸本)9781509046331

Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction. © 2017 IEEE.

关键词： Feedback

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Correcting robot mistakes in real time using EEG signals

Correcting robot mistakes in real time using EEG signals

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IEEE International Conference on robotics and Automation (ICRA)

作者： Andres F. Salazar-Gomez Joseph DelPreto Stephanie Gil Frank H. Guenther Daniela Rus Boston University Guenther Lab Boston MA MIT Distributed Robotics Lab Cambridge MA

关键词： Electroencephalography Real-time systems Collaboration Training Visualization Mobile robots

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Demo Abstract: Panoptes: A Cheap, Extensible, Open-Source Multi-camera Tracking System

Demo Abstract: Panoptes: A Cheap, Extensible, Open-Source Mu...

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International Symposium on Information Processing in Sensor Networks (IPSN)

作者： Manomit Bal Javier Yu Karthik Dantu Distributed Robotics and Networked Embedded Systems Lab (DRONES) Computer Science and Engineering University at Buffalo

ISBN: (纸本)9781467391474

We are developing an extensible, open-source framework that can localize and track rigid bodies using a network of cameras (both RGB and depth). This system is motivated by two design goals - (i) ease of setup, and (ii) ability to be agnostic of individual cameras and recognition algorithms. The goal of this implementation is to be a poor man's motion capture system that can be quickly set up for experimentation and provide accurate 3-D pose of the rigid body and scalable across cameras and the volume of coverage.

关键词： Cameras Robot sensing systems Three-dimensional displays Feature extraction Libraries Calibration Open source software

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