The Experts below are selected from a list of 27588 Experts worldwide ranked by ideXlab platform

Bo Li - One of the best experts on this subject based on the ideXlab platform.

  • 3d fully Convolutional Network for vehicle detection in point cloud
    Intelligent Robots and Systems, 2017
    Co-Authors: Bo Li
    Abstract:

    2D fully Convolutional Network has been recently successfully applied to the object detection problem on images. In this paper, we extend the fully Convolutional Network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows significant performance improvement over the previous point cloud based detection approaches.

  • IROS - 3D fully Convolutional Network for vehicle detection in point cloud
    2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
    Co-Authors: Bo Li
    Abstract:

    2D fully Convolutional Network has been recently successfully applied to the object detection problem on images. In this paper, we extend the fully Convolutional Network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows significant performance improvement over the previous point cloud based detection approaches.

  • 3D fully Convolutional Network for vehicle detection in point cloud
    IEEE International Conference on Intelligent Robots and Systems, 2017
    Co-Authors: Bo Li
    Abstract:

    2D fully Convolutional Network has been recently successfully applied to object detection from images. In this paper, we extend the fully Convolutional Network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows a significant performance improvement over the previous point cloud based detection approaches.

  • Robotics: Science and Systems - Vehicle Detection from 3D Lidar Using Fully Convolutional Network
    Robotics: Science and Systems XII, 2016
    Co-Authors: Bo Li, Tianlei Zhang
    Abstract:

    Convolutional Network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully Convolutional Network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully Convolutional Network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D Convolutional Network. Experiments on the KITTI dataset shows the state-ofthe-art performance of the proposed method.

  • Vehicle detection from 3D lidar using fully Convolutional Network
    Robotics: Science and Systems, 2016
    Co-Authors: Bo Li, Tianlei Zhang, Tian Xia
    Abstract:

    Convolutional Network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully Convolutional Network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully Convolutional Network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D Convolutional Network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.

Zengfu Wang - One of the best experts on this subject based on the ideXlab platform.

  • Video Super-Resolution Using Non-Simultaneous Fully Recurrent Convolutional Network
    IEEE Transactions on Image Processing, 2019
    Co-Authors: Dingyi Li, Zengfu Wang
    Abstract:

    Video super-resolution (SR) aims at restoring fine details and enhancing visual experience for low-resolution videos. In this paper, we propose a very deep non-simultaneous fully recurrent Convolutional Network for video SR. To make full use of temporal information, we employ motion compensation, very deep fully recurrent Convolutional layers, and late fusion in our system. Residual connection is also employed in our recurrent structure for more accurate SR. Finally, a new model ensemble strategy is used to combine our method with a single-image SR method. Experimental results demonstrate that the proposed method is better than that of the state-of-the-art SR methods on quantitative visual quality assessment.

Dingyi Li - One of the best experts on this subject based on the ideXlab platform.

  • Video Super-Resolution Using Non-Simultaneous Fully Recurrent Convolutional Network
    IEEE Transactions on Image Processing, 2019
    Co-Authors: Dingyi Li, Zengfu Wang
    Abstract:

    Video super-resolution (SR) aims at restoring fine details and enhancing visual experience for low-resolution videos. In this paper, we propose a very deep non-simultaneous fully recurrent Convolutional Network for video SR. To make full use of temporal information, we employ motion compensation, very deep fully recurrent Convolutional layers, and late fusion in our system. Residual connection is also employed in our recurrent structure for more accurate SR. Finally, a new model ensemble strategy is used to combine our method with a single-image SR method. Experimental results demonstrate that the proposed method is better than that of the state-of-the-art SR methods on quantitative visual quality assessment.

Haifeng Li - One of the best experts on this subject based on the ideXlab platform.

  • T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
    IEEE Transactions on Intelligent Transportation Systems, 1
    Co-Authors: Ling Zhao, Yujiao Song, Chao Zhang, Pu Wang, Min Deng, Haifeng Li
    Abstract:

    Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an ``open'' scientific issue, owing to the constraints of urban road Network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural Network-based traffic forecasting method, the temporal graph Convolutional Network (T-GCN) model, which is combined with the graph Convolutional Network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road Network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN.

Alexey Shvets - One of the best experts on this subject based on the ideXlab platform.

  • TernausNetV2: Fully Convolutional Network for Instance Segmentation
    2018 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018
    Co-Authors: Vladimir Iglovikov, Selim Seferbekov, Alexander Buslaev, Alexey Shvets
    Abstract:

    The most common approaches to instance segmentation are complex and use two-stage Networks with object proposals, conditional random-fields, template matching or recurrent neural Networks. In this work we present Ternaus- NetV2 - a simple fully Convolutional Network that allows extracting objects from a high-resolution satellite imagery on an instance level. The Network has popular encoderdecoder type of architecture with skip connections but has a few essential modifications that allows using for semantic as well as for instance segmentation tasks. This approach is universal and allows to extend any Network that has been successfully applied for semantic segmentation to perform instance segmentation task. In addition, we generalize Network encoder that was pre-trained for RGB images to use additional input channels. It makes possible to use transfer learning from visual to a wider spectral range. For DeepGlobe-CVPR 2018 building detection sub-challenge, based on public leaderboard score, our approach shows superior performance in comparison to other methods.