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Target Tracking Based on Correlation Filter for Scale Offset

IOP Conference Series: Materials Science and engineering 2020年第1期790卷

作者： Jianhao Tan Xiaoping Ma Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082 China

Existing scale estimation methods mainly use fixed-size tracking box for target tracking. However, when the moving direction of the target changes from near to far or from far to near, the size of the current tracking box cannot adapt to the scale change. In order to overcome the disadvantage of invariant size of tracking box in the scale change, this paper proposes a scale offset estimation method based on correlation filter by combining the state of previous frame and current frame of the target. Select the scale proposal box in the scale layer, and adjust the position and size of the actual tracking box in real time according to the size of the proposal box, target position and scale offset. In this paper, OTB-100 is used as the dataset. The obtained tracking results show that our algorithm Ours has better tracking performance than the existing others tracking algorithm in scale variation, and it has better tracking effect and stronger robustness in drift and occlusion events.

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Dynamic Update Siamese Networks with Deeper Features

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IOP Conference Series: Materials Science and engineering 2020年第1期790卷

作者： Jianhao Tan Shanshan Tan Wang Yin Liming Liu Siyuan Zhang Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082 China

Visual object tracking has been a concern topic these years, and many trackers have achieved good results in various fields. These researches and breakthroughs have made many improvements to solve problems such as drift, lighting, deformation and occlusion. In this paper, we improve the structure of the AlexNet[1] network by designing the three important influencing factors of the receptive field size, total network step size, and feature filling of the twin network. Apart from this, we add a smoothing matrices and a background suppression matrices to effectively learn the features of the first few frames as much as possible. Fuse multilayer feature elements can learn online about target appearance changes and background suppression, and we train them by using continuous video sequences.

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Fast ORB-SLAM without Keypoint Descriptors

arXiv

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arXiv 2020年

作者： Fu, Qiang Yu, Hongshan Wang, Xiaolong Yang, Zhengeng He, Yong Zhang, Hong Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University China The Department of Computing Science University of Alberta Canada Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University China The Southern University of Science and Technology Shenzhen China The School of Mathematics and Information Science Shaanxi Normal University China The Department of Computer Science The University of Western Australia WA6009 Australia

Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 [1] is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe. To overcome these problems, we present FastORB-SLAM which is light-weight and efficient as it tracks keypoints between adjacent frames without computing descriptors. To achieve this, a two stage descriptor-independent keypoint matching method is proposed based on sparse optical flow. In the first stage, we predict initial keypoint correspondences via a simple but effective motion model and then robustly establish the correspondences via pyramid-based sparse optical flow tracking. In the second stage, we leverage the constraints of the motion smoothness and epipolar geometry to refine the correspondences. In particular, our method computes descriptors only for keyframes. We test FastORB-SLAM on TUM and ICL-NUIM RGB-D datasets and compare its accuracy and efficiency to nine existing RGB-D SLAM methods. Qualitative and quantitative results show that our method achieves state-of-the-art accuracy and is about twice as fast as the ORB-SLAM2. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical flows

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Research on Selective filtering and PCNN for Nickel Foam Surface Defect Segmentation

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IFAC-PapersOnLine 2018年第21期51卷 135-140页

作者： Li, Jianqi Zhu, Jiang Nie, Fangyan Cao, Binfang Huang, Lijuan Hunan Province Cooperative Innovation Center for The Construction & Development of Dongting Lake Ecological Economic Zone Hunan University of Arts and Science ChangdeHunan China College of Information Engineering Xiangtan University Xiangtan411105 China National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Changsha410082 China

As the key basis materials of Ni-MH and Ni/Cd battery electrode, the quality of the nickel foam products is closely related with the performance and safety of rechargeable batteries. In order to meet the requirements of automatic defect inspection, this paper proposes an image segmentation method for the appearance defects of nickel foam. Nickel foam image has the characteristics of a low degree of differentiation between foreground and background, basic structure of mutual adhesion, surface morphology for complex random texture features. In order to extract defects from complex nickel foam surface image, a novel selective filtering method is proposed to preprocess the original image, thereby reducing the effects of the above factors, and then a modified PCNN method is used to segment the defect area. The results show that the proposed method can effectively segregate the defects from the background, which lays a foundation for the automatic detection of the appearance quality of nickel foam. © 2018

关键词： Image segmentation Computer vision Nickel metal hydride batteries Surface defects Appearance qualities Automatic defect inspection Automatic Detection Defect inspection modified PCNN Nickel foam Segmentation methods Selective filtering

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A novel hierarchical control strategy for biped robot walking on uneven terrain

A novel hierarchical control strategy for biped robot walkin...

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IEEE-RAS International Conference on Humanoid robots

作者： Chencheng Dong Xuechao Chen Zhangguo Yu Zelin Huang Qingqing Li Qinqin Zhou Qiang Huang School of Mechantronical Engineering Beijing Institute of Technology Beijing China Bionic robot mechanism perception and control laboratory Beijing China Beijing Advanced Innovation Center for Intelligent Robotics and Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

When position-controlled biped robot is blind walking on a uneven terrain at a high speed, huge foot contact impacts will be generated. However, traditional admitance control can't absorb the impact and stabilize the robot due to its slow response and Incompleteness. In this paper, we propose a control strategy including respectively designed swing leg control and support leg control with a new approach of control transition. For Swing leg control, double spring damping model is presented to optimize the admitance controller with faster response and better robustness, and a active foot height controller is also proposed to reduce the impact further. On the other hand, the control transition is accomplished by using a bionic fuzzy control. As a result, the foot contact impact can be reduced and the robot can blind walk fast on uneven terrain. Finally, the validity of the proposed strategy is confirmed by the simulation.

关键词： Legged locomotion Foot Trajectory Springs Adaptation models robot kinematics

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Real time backbone for semantic segmentation

arXiv

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arXiv 2019年

作者： Yang, Zhengeng Yu, Hongshan Fu, Qiang Sun, Wei Jia, Wenyan Sun, Mingui Mao, Zhi-Hong National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China University of Pittsburgh University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering Department of Neurosurgery

The rapid development of autonomous driving in recent years presents lots of challenges for scene understanding. As an essential step towards scene understanding, semantic segmentation thus received lots of attention in past few years. Although deep learning based state-of-the-arts have achieved great success in improving the segmentation accuracy, most of them suffer from an inefficiency problem and can hardly applied to practical applications. In this paper, we systematically analyze the computation cost of Convolutional Neural Network(CNN) and found that the inefficiency of CNN is mainly caused by its wide structure rather than the deep structure. In addition, the success of pruning based model compression methods proved that there are many redundant channels in CNN. Thus, we designed a very narrow while deep backbone network to improve the efficiency of semantic segmentation. By casting our network to FCN32 segmentation architecture, the basic structure of most segmentation methods, we achieved 60.6% mIoU on Cityscape val dataset with 54 frame per seconds(FPS) on 1024 × 2048 inputs, which already outperforms one of the earliest real time deep learning based segmentation methods: ENet. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Theoretical Study of Millimeter Wave Attenuation in Particulate Environment

Theoretical Study of Millimeter Wave Attenuation in Particul...

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UK, Europe, China Millimeter Waves and THz Technology Workshop (UCMMT)

作者： Xiaoming Liu Yixin Zhou Tao Qi Lu Gan Xiaofan Yang School of Physics and Electronic Information Anhui Normal University Anhui Provincial Engineering Laboratory on Information Fusion and Control for Intelligent Robot Wuhu Anhui China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronic and Information System Luoyang Henan China

ISBN: (数字)9781728129938

ISBN: (纸本)9781728129945

Particulate environment is frequently encountered in the millimeter wave propagation path. The particles usually produce scattering and absorption to the millimeter wave. Unfortunately, these particles usually have different size, ingredients and even a mixture of many elements. This paper presents a preliminary study of the attenuation of particles on millimeter wave transmission. The dielectric property of water sand and ice were also discussed. The study was conducted at 110 GHz and 220 GHz. Calculation was based on the Mie theory. A full wave simulation was also conducted to better understand the attenuation effect.

关键词： Millimeter wave technology Attenuation Millimeter wave communication Dielectrics Electromagnetic scattering Ice

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Adaptive robust RBFNNs-based model estimator for a small quadrotor aircraft robot

Adaptive robust RBFNNs-based model estimator for a small qua...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Hang Zhong Shushuai Li Yaonan Wang Hongjian Liu College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology

ISBN: (纸本)9781509061914

This paper presents an on-line estimator that incorporates adaptive MIMO radical basis function neural networks (RBFNNs) for model identification of quadrotor unmanned aerial vehicles (UAVs). The inputs and outputs of quadrotor aircrafts can be obtained from dynamic models or real attitude and position sensors. The adaptive learning rate is employed in the gradient descent method for the update of the weights of RBFNNs, and Lyapunov approach guarantees the stability of the global convergence of the modeling errors. The Welsch functions are also employed as the error functions to get rid of the influence from the noise due to disturbances like wind gusts. Simulation results using robotics Toolbox for Matlab verify the effectiveness and robustness of the proposed estimator compared with results of traditional RBFNNs. Experiment results from real aircraft platform show that RBFNNs combining adaptive learning rate and Welsch error functions can approximate the overall system with high accuracy and robustness to disturbances.

关键词： Atmospheric modeling Aircraft Mathematical model Adaptation models Robustness robots MIMO

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Partial labeled gastric tumor segmentation via patch⇂based reiterative learning

arXiv

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arXiv 2017年

作者： Nan, Yang Coppola, Gianmarc Liang, Qiaokang Zou, Kunglin Sun, Wei Zhang, Dan Wang, Yaonan Yu, Guanzhen College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Laboratory for Robot Vision Perception and Control Hunan University Changsha Hunan410082 China Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Hunan418000 China Faculty of Engineering and Applied Science University of Ontario Institute of Technology OshawaONL1H 7K4 Canada Department of Mechanical Engineering York University TorontoONM3J 1P3 Canada Department of Oncology Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai201203 China

Gastric cancer is the second leading cause of cancer‐related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent. This assignment has high clinical relevance and would generally require vast microscopic assessment by pathologists. Recent advances in deep learning have produced inspiring results on biomedical image segmentation, while its outcome is reliant on comprehensive annotation. This requires plenty of labor costs, for the ground truth must be annotated meticulously by pathologists. In this paper, a reiterative learning framework was presented to train our network on partial annotated biomedical images, and superior performance was achieved without any pre‐trained or further manual annotation. We eliminate the boundary error of patch‐based model through our overlapped region forecast algorithm. Through these advisable methods, a mean intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09% on the partial labeled dataset was achieved, which made us win the 2017 China Big Data & Artificial Intelligence Innovation and Entrepreneurship Competitions1. Copyright © 2017, The Authors. All rights reserved.

关键词： Diseases

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