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RA Loss: Relation-Aware Loss for Robust Person Re-identification 16th

RA Loss: Relation-Aware Loss for Robust Person Re-identific...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Wang, Kan Hu, Shuping Cheng, Jun Cheng, Jun Pang, Jianxin Tan, Huan Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robot Localization and Navigation Technology UBTech Robotics Corp Ltd. Shenzhen China Institute for Infocomm Research A*STAR Singapore Singapore

ISBN: (纸本)9783031262838

Previous relation-based losses in person re-identification (ReID) typically comprise two sequential steps: they firstly sample both positive pair and negative pair and then deploy constraints to simultaneously improve intra-identity compactness and inter-identity separability. However, existing relation-based losses usually place emphasis on exploring the relation between images and therefore consider only several pairs during each optimization. This inevitably leads to different convergence status for pairs of the same kind and brings about the intra-pair variance problem. Accordingly, we propose a novel Relation-Aware (RA) loss to address the intra-pair variance via exploring the informative relation across pairs. In brief, we introduce a macro-constraint and a micro-constraint. The macro-constraint encourages the separation of positive pair and negative pair via pushing far apart the two "centers" of the positive pair and the negative pair. The "center" of each kind of pair are obtained via averaging all the pairs of the same kind. The micro-constraint further enhances the compactness by minimizing the discrepancies among pairs of the same kind. The two constraints work cooperatively to relieve the intra-pair variance and improve the quality of pedestriansąŕ representation. Results of extensive experiments on three widely used ReID benchmarks, i.e., Market-1501, DukeMTMC-ReID and CUHK03, demonstrate that the RA loss brings improvements over existing relation-based losses. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Modified Deeplabv3+ Model Based on Polarized Self-Attention Mechanism

A Modified Deeplabv3+ Model Based on Polarized Self-Attentio...

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第42届中国控制会议

作者： Zheng Zhang Rui Song Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University

In this paper,we propose a modified Deeplabv3+ model based on the combination optimization of atrous convolutions and Polarized Self-Attention module,aiming to enhance the semantic segmentation performance of small-scale objects in various *** improvements of this paper mainly focus on the following two ***,we optimize the combination of atrous convolutions in the Deeplabv3+model by using lower atrous rates,so that it can expand the receptive field while enhancing the recognition ability of small-scale ***,by introducing the Polarized Self-Attention module,the semantic segmentation of small objects is further optimized,while capturing more global semantic information to ensure overall semantic *** experimental results demonstrate that,compared with the original Deeplabv3+ network model,the modified model improves the segmentation accuracy by 1.8% and 1.5% on the Cityscapes and Pascal VOC 2012 test datasets.

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Multi-kernel-size Convolutional Supervised Autoencoders for Tactile Gesture Recognition 12

Multi-kernel-size Convolutional Supervised Autoencoders for ...

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12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Ye, Chaoxiang Li, Xiaoyu Huang, Binhua Su, Yuanzhe Mi, Tingting Zhou, Zhenning Yi, Zhengkun Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

With the development of human-computer interaction, tactile gesture recognition has been widely used in our life. To solve the problem of overfitting on the sample-limited tactile datasets, we apply the Supervised Autoencoders (SAE) to improve the generalization performance. Moreover, based on the SAE, we propose the Multi-kernel-size Convolutional Supervised Autoencoders (MCSAE) to further improve the generalization performance on the limited dataset, which provides models with more structure of receptive fields and enhances the feature extraction ability of SAE. In comparison with other state-of-the-art (SOTA) models, the SAE we apply has higher gesture recognition accuracy and MCSAE can further improve the generalization performance of SAE on the sample-limited publicly available dataset. © 2022 IEEE.

关键词： Gesture recognition

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High-Accuracy Pose Estimation for 3D Reconstruction Considering the Manipulator Motion Constraints with an Eye-in-Hand RGB-D Camera 42

High-Accuracy Pose Estimation for 3D Reconstruction Consider...

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42nd Chinese Control Conference, CCC 2023

作者： Zhou, Yanling Zhang, Xuebo Wang, Runhua Zhang, Shiyong Yuan, Mingxing Yuan, Jing Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education China

ISBN: (纸本)9789887581543

Three-dimensional(3D) environmental reconstruction of the manipulator workspace is a prerequisite to enable autonomous collision-free path planning and subsequent task execution, wherein the positioning accuracy of the equipped sensor is one key issue to determine the performance of 3D reconstruction. In this paper, an eye-in-hand RGB-D camera is used to complete the manipulator workspace scanning and a novel high-accuracy pose estimation method for 3D reconstruction is proposed, which not only considers the geometric and photometric constraints constructed from the visual information used in traditional methods, but also considers the manipulator motion constraints since the positioning accuracy of the manipulator is usually high. Based on above three constraints, three error functions are constructed respectively and fused with different weights to form an integrated objective function. Then, the problem is converted into solving the least squares problem by the Gaussian Newton nonlinear iterative method, which involves the computation of Jacobian matrices and residuals of the three error functions. To this end, the mathematical derivation of their formulas considering the manipulator motion constraints are given in detail. By qualitative and quantitative experimental comparison with traditional methods, the proposed method can greatly improve the accuracy of pose estimation and 3D reconstruction, which always maintains the error within 3 cm, and show good robustness even in the challenging environments with weak texture or geometric features. (Supplemented video link: https://***/FkFTxssnWms) © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Manipulators

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A Superquadrics-based Steering Angle Obstacle Avoidance Method of DMPs

A Superquadrics-based Steering Angle Obstacle Avoidance Meth...

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第42届中国控制会议

作者： Zezhi Liu Yongchun Fang Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University

In this paper, we propose a new method for using Dynamic Movement Primitives(DMPs) to enable robots to avoid volumetric obstacles when learning skills from human demonstrations. We can utilize coupling terms in DMPs to modify the planned trajectory of the end-effector. Our method combines superquadrics and a steering angle scheme to construct new coupling terms for obstacle avoidance, without relying on potential fields. This guarantees the convergence of the dynamic system. Additionally, we modify the original center point steering angle formula to improve the ability to avoid obstacles in a wider range of angles. Our proposed method is applicable to most types of superquadrics, including those with flat boundaries,and we validate it through simulations.

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Adaptive Adjustment of Factor's Weight for a Multi-Sensor SLAM 2

Adaptive Adjustment of Factor's Weight for a Multi-Sensor SL...

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2022 2nd International Conference on Computer, Internet of Things and Control Engineering, CITCE 2022

作者： Zhu, Zihan Zhang, Yi Wang, Weijun Feng, Wei Luo, Haowen Zhang, Yaojie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences 518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Construction Robotics and Intelligent Manufacturing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial International Joint Research Center for Robotics and Intelligent Construction Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

A multi-sensor fusion simultaneous localization and mapping(SLAM) method based on factor graph optimization that can adaptively modify the weight of the graph factor is proposed in this study, to enhance the localization and mapping capability of autonomous robots in tough situations. Firstly, the algorithm fuses multi-lines lidar, monocular camera, and inertial measurement unit(IMU) in the odometry. Second, the factor graph is constructed using lidar and visual odometry as the unary edge and binary edge constraints, respectively, with the motion determined by IMU odometry serving as the primary odometry in the system. Finally, different increments of IMU odometry, lidar odometry and visual odometry are computed as favor factors to realize the adaptive adjustment of the factor's weight. The suggested method has greater location accuracy and a better mapping effect in complex situations when compared to previous algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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An Autoencoder-Based Feature Extraction Method Applied to the Detection of Lateral Walking Gait Phase 1

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Yang, Lijun Xiang, Kui Pang, Muye Luo, Mingxiang Yin, Meng Li, Wenju Cao, Wujing School of Automation Wuhan University of Technology Wuhan430070 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518005 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

The traditional method of extracting time-domain features is subjective and cumbersome. An autoencoder-based feature extraction method is proposed to overcome these shortcomings. Experiments were conducted to detect gait phases of lateral walking of 10 subjects at various gait cycles (rhythms) and strides, which combined into 9 lateral walking modes. Based on the stacked autoencoder with dropout (AE-dropout), different quantities of features from the original data were extracted. Subsequently, these features were used to detect the phase of lateral walking gait based on decision tree (Tree), k-Nearest Neighbors (KNN), support sector machines (SVM), and artificial neural network (ANN). The results demonstrated that the KNN, SVM, and ANN achieve higher accuracy after using the features extracted by our method for the detection of lateral walking gait phase. Moreover, with the increase of the number of extracted features, the cross-subject accuracy of the four models (AE-dropout + X, X represents Tree, KNN, SVM, and ANN) exhibit similar regularities, which increased first and then decreased. In addition, compared to the method of extracting time-domain features, our method has a smaller standard error of mean (SEM) in cross-subject recognition accuracy for different lateral walking modes. It proves that our method has greater generalization capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Feature extraction

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Person Following with Dynamic Obstacle Avoidance

Person Following with Dynamic Obstacle Avoidance

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Kong, Lingxin Jiang, Guolai Tang, Yifeng Ou, Yongsheng Xu, Sheng China Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350327182

Person following is essential for many human-robot interaction applications. However, many existing person-following methods do not consider obstacles that have a serious impact on the person-following. In this paper, we focus on the obstacle avoidance problem in the person-following process and propose a dynamic obstacle avoidance path planning method. Our method integrated trajectory prediction into global and local planning to follow a person and avoid obstacles. The proposed method is applied to a practical robot person-following task as an experiment. The experimental results show that the proposed method can achieve the target person following robustly in the case of obstacles. © 2023 IEEE.

关键词： Human robot interaction

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A Scene Classification Method Based on Improved Incremental Brain-like Developmental Model

A Scene Classification Method Based on Improved Incremental ...

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第42届中国控制会议

作者： Hanqian Luo Yongchun Fang Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University

With the rapid development of machine learning, more and more vision-based scene classification methods are being applied to mobile robots to help them perform complex tasks such as their self-localization and navigation decisions. Most of today's scene classification algorithms are based on the batch learning approach, which often suffers from the inability to effectively process time-series data sets and models that are too complex and not scalable. To address the shortcomings and deficiencies of batch learning algorithms, this paper proposes an improved incremental brain-like developmental model, which can store the knowledge learned from the external environment in the neurons inside the model by simulating the activation of neurons in the human brain, and can learn and understand similar new knowledge based on the lobe component analysis(LCA).The model implements positive and negative feedback regulation mechanisms through the response competition mechanism of internal neurons. In this paper, the weight updating algorithm of the improved model can incrementally change the learning rate of the model and improve the accuracy of the scene classification task. Experimental results show that the model proposed in this paper has good recognition accuracy in indoor scene classification task. Compared with other traditional image classification algorithms for batch data processing, this model has better computational real-time performance by incremental learning of time-series data in the environment, which can meet the needs of mobile robots to handle complex scene classification tasks.

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A Non-Calibration Nonlinear Errors Correction Method for Phase-Shifting Patterns

A Non-Calibration Nonlinear Errors Correction Method for Pha...

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Chinese Automation Congress (CAC)

作者： Yujie Zheng Bin Chen Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

To reduce phase errors caused by nonlinear responses in the measurement system, we propose a non-calibration correction method for nonlinear errors in phase-shifting patterns. First, the captured grating fringes are transformed using a Fourier transform to convert the images from the gray scale to the frequency domain. The first harmonic frequency is then determined using an appropriate method. Second, the appropriate filter cutoff frequency is calculated by combining the phase-shift algorithm's sensitivity to different harmonics with frequency response curves. Low-pass filtering is then performed on the phase-shift patterns to suppress high frequencies caused by nonlinear responses, thus reducing phase errors in the actual measurement process. Finally, the standard plane and other irregularly shaped objects were reconstructed using the proposed method and other techniques. Measurement errors under different conditions were then compared. Experimental results show that the proposed method significantly suppresses gamma nonlinear responses and achieves high measurement accuracy and efficiency.

关键词： Optical filters Measurement errors Phase measurement Nonlinear distortion Measurement uncertainty Low-pass filters Optical distortion Harmonic analysis Distortion measurement Power harmonic filters

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