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检索条件"机构=Guangdong Provincial Key Lab of Robotics and Intelligent System"

共 438 条记录，以下是131-140 订阅

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Multi-scale analysis of contextual information within spatio-temporal video volumes for anomaly detection

Multi-scale analysis of contextual information within spatio...

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IEEE International Conference on Image Processing

作者： Nannan Li Huiwen Guo Dan Xu Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Chinese Academy of Sciences China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781479957521

In this paper, we present a novel approach for video anomaly detection in crowded scenes. The proposed approach detects anomalies based on the contextual information analysis within spatio-temporal video volume. Around each pixel, spatio-temporal volumes are built and clustered to construct the activity pattern codebook. Then, the composition information of the volumes within a large spatiotemporal window is described via a dictionary learned by sparse representation. Furthermore, multi-scale analysis is employed to adapt the size change of abnormal events. Finally, the sparse reconstruction cost is designed to evaluate the abnormal level of an input motion pattern. We demonstrate the efficiency of the proposed method on the existing public available anomaly-detection datasets and the performance comparasion with three existing methods validates that the proposed method detects anomalies more accurately.

关键词： Vectors Dictionaries Hidden Markov models Computer vision Trajectory Cameras Feature extraction

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Rapid disparity prediction for dynamic scenes

Rapid disparity prediction for dynamic scenes

引用

9th International Symposium on Advances in Visual Computing, ISVC 2013

作者： Jiang, Jun Cheng, Jun Chen, Baowen Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Chinese University of Hong Kong Hong Kong Hong Kong Shsenzhen Institute of Information Technology China Guangdong Provincial Key Laboratory of Robotics and Intelligent System China Shenzhen Key Laboratory of Computer Vision and Pattern Recognition China

ISBN: (纸本)9783642419133

Real-time 3D sensing plays a critical role in robotic navigation, video surveillance and human-computer interaction, etc. When computing 3D structures of dynamic scenes from stereo sequences, spatiotemporal stereo and scene flow methods can produce temporally coherent disparity. However, most existing methods do not utilize the previous disparity map sufficiently to compute the next disparity map, and the searching space of correspondences limits the speed of disparity computation for each image pair. This paper proposes an effective scheme to predict disparity maps from stereo sequences. In particular, we apply a robust 3D registration algorithm based on the angular-invariant feature to estimate the ego-motion of the stereo rig between consecutive frames, and present the transformation between consecutive disparity maps. The scheme can produce a sequence of temporally coherent disparity maps rapidly. We apply the new scheme to real outdoor scenes, and thorough empirical studies indicate the effectiveness of the new scheme for practical applications. © 2013 Springer-Verlag.

关键词： Human computer interaction

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LOCAL MEAN SPATIO-TEMPORAL FEATURE FOR DEPTH IMAGE-BASED SPEED-UP ACTION RECOGNITION

LOCAL MEAN SPATIO-TEMPORAL FEATURE FOR DEPTH IMAGE-BASED SPE...

引用

IEEE International Conference on Image Processing

作者： Xiaopeng Ji Jun Cheng Dapeng Tao Shenzhen Institutes of Advanced Technology CAS The Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Robotics and Intelligent System

ISBN: (纸本)9781479983407

With the promptly growing population of the low-cost Microsoft Kinect sensor, action recognition, which is a hard yet important problem in computer vision, has been received substantial attention. However, most existing approaches in action recognition spend much time on feature detection even though these methods can achieve high recognition rates. In this paper, we propose a local mean spatio-temporal feature (LMSF) to speed up depth image based action recognition. In particular, we solve the problem from three aspects: (1) associate the 4D normals by a local mean spatiotemporal neighborhood;(2) extract motion frames by detecting the differences between consecutive frames;(3) reduce redundant normals extracted from depth cloud points by sparse coding. The proposed approach is tested on two public benchmark datasets, i.e., MSRAction3D and MSRGesture3D. Experimental results demonstrate the advantages of our improvement method and the state-of-the-art performance on processing speed.

关键词： Speed-up action recognition Depth sequences Polynormal Mean spatio-temporal neighborhood images computer vision feature detections Recognition (Psychology) Frame

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A one-step pseudolinear kalman filter for invasive target tracking in three-dimensional space

A one-step pseudolinear kalman filter for invasive target tr...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Zhang, Ying Liang, Rui Xu, Sheng Zhang, Liyuan Zhang, Ye Xiao, Dandan State Grid Tianjin Electric Power Company City West Power Supply Branch Tianjin300190 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

This work studies the characteristics of the 3-dimensional (3D) pseudolinear Kalman filter (PLKF) proposed in the previous work [1] for angle-of-arrival (AOA) target tracking in the 3D space. The previous two-step 3D PLKF contains an xy-PLKF and a z-PLKF. This paper develops a one-step PLKF algorithm, which has low computational complexity. The estimation performance analysis of these two different PLKFs is provided. The one-step PLKF sacrifices the estimation accuracy to acquire a low computational complexity. In the distributed target tracking problem, the one-step PLKF can save a lot of communication costs compared with the two-step one. Furthermore, the stability of two PLKFs are also been simply proven. Simulation examples verify the effectiveness of the proposed one-step 3D PLKF to track an invasive target. Besides, the difference between the two PLKFs based on the theoretical analysis is also demonstrated by the simulation examples. © 2021 IEEE.

关键词： Target tracking

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Dynamic modeling and vibration analysis of a 6-DOFs industrial robot considering joint flexibility

Dynamic modeling and vibration analysis of a 6-DOFs industri...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Shen, Guodong Xu, Sheng Chen, Chunjie Wang, Qiang Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

For the industrial robots, the vibration caused by the joint flexibility could affect the manipulator's performance significantly. Therefore, the modeling and vibration analysis of industrial robot considering joint flexibility has become an important topic in recent years. However, the research on flexible-joint manipulator is usually performed for planar robots, whereas the research on general spatial multi degrees of freedom (DOFs) industrial robots are still limited. In this paper, the dynamic modeling approach is developed for vibration analysis of the serial industrial robot having six DOFs. Firstly, based on the Spong's model of joint deformation, the flexibility of all joints is considered concurrently, and the partial velocity relationship between each link and the joint are deduced. Then, the dynamic model of the 6-DOFs flexible robot is derived according to the Kane equation. The results using the proposed Kane-based method are verified by several numerical simulations. Finally, vibration responses of the end-effector under different joint stiffness parameters are analyzed. The results in this study can offer some useful guidance for the path planning and control to suppress the vibration of the multi-DOFs industrial robots. © 2021 IEEE.

关键词： Vibration analysis

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A deep multi-view learning method for rice grading

A deep multi-view learning method for rice grading

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Chen, Yuqi Wu, Yiqiang Cheng, Jun Tao, Dapeng FIST LAB School of Information Science and Engineering Yunnan University Kunming Yunnan650091 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Chinese University of Hong Kong Hong Kong999077 Hong Kong

ISBN: (纸本)9781728137261

- Rice grading has achieved raising attention in recent years for its importance in food security, whereas progress is limited. The difficulty in this topic says that the rice kernels are crowed in the visual field of, say a camera, which makes the detection of a single kernel hard. In this paper, we are based on a newly designed rice streaming system and propose a novel rice grading model. The streaming system snapshots a kernel from three different visual directions, producing three images of a single kernel. The FIST-Model analyses these images by employing a multi-view learning method which minimizes the information loss and generates an intact representation of the rice kernel. Such a representation remains strong discriminability and is effective to determine the grading level of the rice. Finally, we evaluate the performance of the proposed FIST-Model on the FIST-Rice dataset by setting a series of experiments based on different deep learning models. The result indicates that the FIST-Model has a superior performance over previous rice grading model. © 2018 IEEE.

关键词： Grading

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Low illumination enhancement for object detection in self-driving

Low illumination enhancement for object detection in self-dr...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Qu, Yangyang Ou, Yongsheng Xiong, Rong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-MachineIntelligence-Synergy Systems ShenzhenInstitutes of Advanced Technology China

ISBN: (纸本)9781728163215

Object detection plays an important role in the field of self-driving. Illumination has a great impact on object detection, but most of the current methods do not solve the problem of object detection in poor light environment well. We propose a network that can optimize the image conversion which is based on the Cycle Generative Adversarial Networks (CycleGAN). We redesign the discriminator network of Cycle-GAN, add additional discriminators, optimize multiple parts of the network such as loss functions, and add object detection networks after converting the network. The Robot Car dataset of Oxford University is utilized to verify the effectiveness of the proposed method, and the results proved that our method can significantly improve the detection accuracy and increase the detected object number in low illumination environment. © 2019 IEEE.

关键词： Image enhancement

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A method for generating large-scale high definition color-point map

A method for generating large-scale high definition color-po...

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Li, Yuxiao Wang, Can Chen, Fan Su, Zhilong Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technolog Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781728137261

We propose a method to reconstruct a High-Definition Color-Pointed map from the large-scale urban road scene. In contrast to prior methods, we collect the urban road data under the severe traffic conditions and several kilometers long data sets. In our High-Definition Color-Pointed map, we can clearly see the lane surface and surrounding environment. Moreover, our maps are collected in a high speed of 40km/h, which greatly improved the practicality of the whole system. The High-Definition Color-Pointed map can be used in perception, localization and automatic navigation in driverless cars, drones and smart city management. Images, lidar and gnss best poses are collected as the raw data. We use interpolation method to process the raw data, and use our algorithm to translate the Lidar poses into the images collected time. To detect the dynamic obstacle, we use the fast neural framework YOLOv2. Our method, Dislocation Projection, can solve the spare points problems. Our algorithm was evaluated on wide roads and narrow streets. The experimental results exhibited the effectiveness of the proposed approach. © 2019 IEEE.

关键词： Optical radar

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Semantic Segmentation Model for Road Scene Based on Encoder-Decoder Structure

Semantic Segmentation Model for Road Scene Based on Encoder-...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Peng, Yuanzhe Han, Weichao Ou, Yongsheng Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Semantic segmentation as a pixel-wise segmentation task provides rich object information, which is an important research topic in robotic perception. It has been widely applied in many fields, such as autonomous driving and robot navigation. In the application of understanding road scene, the semantic segmentation model should accurately describe the appearance and shape of different categories of objects. In addition, the semantic segmentation model need to understand the spatial relationships between different categories. In order to improve the performance of semantic segmentation model for road scene, we present a model based on encoder-decoder structure with dilated convolution. We apply this model on the Cityscapes dataset and compare it with other classical models. To assess performance, we rely on the standard Jaccard Index IoU (Intersection over Union) and mIoU (mean Intersection over Union). The experimental results verify that this model can effectively improve the performance of semantic segmentation and meet the requirements for road scene. © 2019 IEEE.

关键词： Semantic Segmentation

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A data-driven shared control system for exoskeleton rehabilitation robot

A data-driven shared control system for exoskeleton rehabili...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Li, Feng He, Yong Li, Jinke Ni, Jiangpeng Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

It is very important to establish a control operating system for rehabilitation robots, especially to improve the rehabilitation effect and human-machine interaction ability of patients. Exoskeletons have been proved to be effective in providing highly repeatable and accurate rehabilitation exercises, but most existing exoskeletons have inconsistent operating systems and data loss. This paper designed a novel data-driven shared control system(DDSCS) farmework, applied to different exoskeleton rehabilitation robots (ERR). Due to the unique physical characteristics of exoskeleton rehabilitation robots, it can't be adapt to different patients. Firstly, the DDSCS framework is established via the data-driven and shared technology, and the feasibility is analyzed. Secondly, to iterate the individualized gait trajectory, data-driven gait trajectory correction model is designed. Finally, the shared human-machine interface is developed, and the superiority and effectiveness of DDSCS framework are verified by the exoskeleton robot experiments. © 2021 IEEE.

关键词： Control systems

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