News | People | Research | Publications | Datasets | Positions | Teaching | Contact

Guang Chen 陈广

Guang Chen

Research Professor, Ph.D Advisor

Tongji University


Email: guangchen(at)tongji.edu.cn or guang(at)in.tum.de

Address: NO.4800, Cao'an Road, Jiading District, Shanghai

Homepage: https://ispc-group.github.io/


Welcome to the website of the Intelligent Sensing, Perception, and Computing Group led by Research Prof. Guang Chen. Our lab was founded in February 2018 and is part of the Advanced Research Institute,Institute of Intelligent Vehicle, and School of Automotive Studies, at the Tongji University. Prof. Guang Chen is also a core member of the CAE academician, Prof. Zhihua Zhong' research team at Tongji University. Our lab members are from Tongji University and the Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich, and jointed advised by Prof. Guang Chen, Prof. Alois Knoll and Prof. Zhihua Zhong (CAE academician, former president of Tongji University).

News

Pre-Prints

LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection
Sanqing Qu, Guang Chen*, Dan Xu, Jinhu Dong, Fan Lu, Alois Koll
Arxiv, 2020
code / video / arXiv / bibtex
@misc{qu2020lapnet,
      title={LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection}, 
      author={Sanqing Qu and Guang Chen and Dan Xu and Jinhu Dong and Fan Lu and Alois Knoll},
      year={2020},
      eprint={2011.07915},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
  

Peer-Reviewed Paper (Selected)

MoNet: Motion-based Point Cloud Prediction Network
Fan Lu, Guang Chen*, Zhijun Li, Lijun Zhang, Yinlong Liu, Sanqing Qu, Alois Knoll
IEEE Transactions on Intelligent Transportation Systems, 2021
arXiv / code / video / bibtex
@misc{lu2020monet,
      title={MoNet: Motion-based Point Cloud Prediction Network}, 
      author={Fan Lu and Guang Chen and Yinlong Liu and Zhijun Li and Lijun Zhang and Yinlong Liu and Sanqing Qu and Alois Knoll},
      journal={IEEE Transactions on Intelligent Transportation Systems},
      year={2021},
      publisher={IEEE}
}
  
In this paper, we propose a novel motion-based neural network named MoNet. The key idea of the proposed MoNet is to integrate motion features between two consecutive point clouds into the prediction pipeline. The introduction of motion features enables the model to more accurately capture the variations of motion information across frames and thus make better predictions for future motion.
HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration
Fan Lu, Guang Chen*, Yinlong Liu, Lijun Zhang, Sanqing Qu, Shu Liu, Rongqi Gu
International Conference on Computer Vision (ICCV), 2021
project / code / video / bibtex
@article{ispc:hregnet,
  author  = {Fan Lu and Guang Chen and Yinlong Liu and Lijun Zhang and Sanqing Qu and Shu Liu andand Rongqi Gu},
  title   = {HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration},
  journal = {International Conference on Computer Vision (ICCV)},
  year    = {2021},
}
    
we propose an efficient hierarchical network named HRegNet for large-scale outdoor LiDAR point cloud registration. Instead of using all points in the point clouds, HRegNet performs registration on hierarchically extracted keypoints and descriptors. Bilateral consensus and neighborhood consensus are introduced to improve the robustness and accuracy.
HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction
Ya Wu, Guang Chen*, Zhijun Li, Lu Xiong, Zhengfa Liu, Alois Knoll
IEEE Transactions on Vehicular Technology, 2021
project / code / video / bibtex
@article{ispc:tvt-l-2021,
  author  = {Ya Wu and Guang Chen and Zhijun Li and Lu Xiong and Zhengfa Liu and Alois Knoll},
  title   = {HSTA: A Hierarchical Spatio-Temporal Attention Model for Trajectory Prediction},
  journal = {IEEE Transactions on Vehicular Technology (TVT)},
  year    = {2021},
}
    
we propose a Hierarchical Spatio-Temporal Attention architecture (HSTA), which activates the utilization of spatial interactions with different weights, and jointly considers the temporal interactions across time steps of all agents. More specially, the graph attention mechanism (GAT) is presented to capture spatial interactions, the multi-head attention mechanism (MHA) is conducted to encode temporal correlations of interactions and a state gated fusion (SGF) layer is used to integrate spatial and temporal interactions..
Pole-Curb Fusion based Robust and Efficient Autonomous Vehicle Localization System with Branch-and-Bound Global Optimization and Local Grid Map Method
Guang Chen, Fan Lu, Zhijun Li*, Yinlong Liu, Jinhu Dong, Junqiao Zhao, Junwei Yu, Alois Knoll
IEEE Transactions on Vehicular Technology, 2021
project / code / video / bibtex
@article{ispc:tvt-l-2021,
  author  = {Guang Chen and Fan Lu and Zhijun Li and Yinlong Liu and Jinhu Dong and Junqiao Zhao and Junwei Yu and Alois Knoll},
  title   = {Pole-Curb Fusion based Robust and Efficient Autonomous Vehicle Localization System with Branch-and-Bound Global Optimization and Local Grid Map Method},
  journal = {IEEE Transactions on Vehicular Technology (TVT)},
  year    = {2021},
}
    
we propose a novel lightweight LiDAR-based localization system for autonomous vehicle in this paper. The proposed system only relies on lightweight poles and curbs landmark map, which is highly robust and efficient compared to other localization systems.
Fusion-based Feature Attention Gate Component for Vehicle Detection based on Event Camera
Hu Cao, Guang Chen*, Jiahao Xia, Genghang Zhuang, Alois Knoll
IEEE Sensors Journal, 2021
project / code / video / bibtex
@article{ispc:sensors2021,
  author  = {Hu Cao and Guang Chen and Jiahao Xia and Genghang Zhuang and Alois Knoll},
  title   = {Fusion-based Feature Attention Gate Component for Vehicle Detection based on Event Camera},
  journal = {IEEE Sensors Journal},
  year    = {2021},
}
    
we introduce a fully convolutional neural network with feature attention gate component (FAGC) for vehicle detection by combining frame-based and event-based vision. Both grayscale features and event features are fed into feature attention gate component (FAGC) to generate the pixel-level attention feature coefficients to improve the feature discrimination ability of the network.
Active Safety Control of Automated Electric Vehicles at Driving Limits: A Tube-based MPC Approach
Peng Hang, Xin Xia, Guang Chen, Xinbo Chen
IEEE Transactions on Transportation Electrification, 2021
project / code / video / bibtex
@article{ispc:tai21,
  author  = {Peng Hang and Xin Xia and Guang Chen and Alois Knoll},
  title   = {Active Safety Control of Automated Electric Vehicles at Driving Limits: A Tube-based MPC Approach},
  journal = {IEEE Transactions on Transportation Electrification},
  year    = {2021},
}
    
The motion control problem of AEVs at driving limits is studied in this paper. To address this issue, an integrated controller is designed based on Tube-based MPC control algorithm. To advance AEVs’ path tracking performance and handling stability simultaneously, 4WS and DYC techniques are applied.
KAM-Net: Keypoint-Aware and Keypoint-Matching Network for Vehicle Detection from 2D Point Cloud
Tianpei Zou, Guang Chen*, Zhijun Li, Wei He, Sanqing Qu, Shangding Gu, Alois Knoll
IEEE Transactions on Artificial Intelligence (minor revision), 2021
project / code / video / bibtex
@article{ispc:tai21,
  author  = {Tianpei Zou and Guang Chen and Zhijun Li and Wei He and Sanqing Qu and Shangding Gu and Alois Knoll},
  title   = {KAM-Net: Keypoint-Aware and Keypoint-Matching Network for Vehicle Detection from 2D Point Cloud},
  journal = {IEEE Transactions on Artificial Intelligence },
  year    = {2021},
}
    
...
Globally Optimal Consensus Maximization for Relative Pose Estimation With Known Gravity Direction
Yinlong Liu, Guang Chen*, Rongqi Gu, Alois Knoll
IEEE Robotics and Automation Letters ( Volume: 6, Issue: 3, July 2021)
project / code / video / bibtex
@article{ispc:ral21,
  author  = {Yinlong Liu and Guang Chen and Rongqi Gu and Alois Knoll},
  title   = {Globally Optimal Consensus Maximization for Relative Pose Estimation With Known Gravity Direction},
  journal = {IEEE Robotics and Automation Letters },
  year    = {2021},
}
    
We propose a globally optimal algorithm for relative pose estimation with known gravity direction. Specifically, the proposed method employs the branch-and-bound algorithm to solve a consensus maximization problem, and thus it is able to obtain the global solution with a provable guarantee.
VCANet: Vanishing-Point-Guided Context-Aware Network for Small Road Object Detection
Guang Chen*, Kai Chen, Lijun Zhang*, Liming Zhang, Alois Knoll
Automotive Innovation, 2021
project / code / video / bibtex
@article{ispc:vcanet21,
  author  = {Guang Chen and Kai Chen and Lijun Zhang and Liming Zhang and Alois Knoll},
  title   = {VCANet: Vanishing-Point-Guided Context-Aware Network for Small Road Object Detection},
  journal = {Automotive Innovation},
  year    = {2021},
}
    
This paper is the first attempt to establish a large TJ-LDRO dataset, which consists of 109,337 images from real and virtual simulation environment, labeled in detail. Besides, the Vanishing-point-guided Context-Aware Network (VCANet) is introduced for small object detection.
PointINet: Point Cloud Frame Interpolation Network
Fan Lu, Guang Chen*, Sanqing Qu, Zhijun Li, Yinlong Liu, Alois Knoll
Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
project / code / video / bibtex
@article{ispc:pointinet,
  author  = {Fan Lu and Guang Chen and Sanqing Qu and Zhijun Li and Yinlong Liu and Alois Knoll},
  title   = {PointINet: Point Cloud Frame Interpolation Network},
  journal = {AAAI},
  year    = {2021},
}
    
We propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). Based on the proposed method, the low frame rate point cloud streams can be upsampled to higher frame rates.
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
Fan Lu, Guang Chen*, Yinlong Liu, Zhongnan Qu, Alois Knoll
Advances in Neural Information Processing Systems (NeurIPS), 2020
code / video / bibtex
@article{ispc:rskddnet,
  author  = {Fan Lu and Guang Chen and Yinlong Liu and Zhongnan Qu and Vittorio Murino},
  title   = {RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor},
  journal = {NeurIPS},
  year    = {2020},
}
    
This paper proposes Random Sample-based Keypoint Detector and Descriptor Network (RSKDD-Net) for large scale point cloud registration. The key idea is using random sampling to efficiently select candidate points and using a learning-based method to jointly generate keypoints and descriptors.
Globally Optimal Camera Orientation Estimation from Line Correspondences by BnB algorithm
Yinlong Liu, Guang Chen*, Alois Knoll
IEEE Robotics and Automation Letters (RA-L), 2020
code / video / bibtex
@article{liu2020globally,
  title={Globally Optimal Camera Orientation Estimation from Line Correspondences by BnB algorithm},
  author={Liu, Yinlong and Chen, Guang and Knoll, Alois},
  journal={IEEE Robotics and Automation Letters},
  volume={6},
  number={1},
  pages={215--222},
  year={2020},
  publisher={IEEE}
}
    
We propose a globally optimal camera orientation estimation algorithms. We decouple the rotation and translation estimation of a PnL problem by considering the geometrical property. The BnB algorithm is applied and it globally searches the entire rotation space to obtain the optimal camera orientation.
Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods
Guang Chen*, Fa Wang, Sanqing Qu, Junwei Yu, Xiangyong Liu, Lu Xiong, Alois Knoll
IEEE Transactions on Intelligent Transportation Systems, 2020
code / video / dataset / bibtex
@article{chen2020pseudo,
  title={Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods},
  author={Chen, Guang and Wang, Fa and Qu, Sanqing and Chen, Kai and Yu, Junwei and Liu, Xiangyong and Xiong, Lu and Knoll, Alois},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2020},
  publisher={IEEE}
}
    
We propose a learning based method with the input of pseudo-images, named Cascade Pyramid Region Proposal Convolution Neural Network (Cascade Pyramid RCNN), and a hybrid learning method with the input of sparse points, named Hybrid Resnet Lite.
NeuroIV: Neuromorphic Vision Meets Intelligent Vehicle Towards Safe Driving With a New Database and Baseline Evaluations
Guang Chen*, Fa Wang, Weijun Li, Lin Hong, Jorg Conradt, Jieneng Chen, Zhenyan Zhang, Yiwen Lu, Alois Knoll
IEEE Transactions on Intelligent Transportation Systems, 2020
code / video / bibtex
@article{chen2020neuroiv,
  title={NeuroIV: Neuromorphic vision meets intelligent vehicle towards safe driving with a new database and baseline evaluations},
  author={Chen, Guang and Wang, Fa and Li, Weijun and Hong, Lin and Conradt, J{\"o}rg and Chen, Jieneng and Zhang, Zhenyan and Lu, Yiwen and Knoll, Alois},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2020},
  publisher={IEEE}
}
    
In this work, we build the first-ever database, NeuroIV, and provides some baseline evaluations that bridges the gap between neuromorphic engineering and intelligent vehicle research. The NeuroIV introduces new ways to sense and perceive the environment that brings new revolution of vision-based perception system in intelligent vehicle. It will serve as a standardized and open-source platform on which new neuromorphic vision based methods can be developed and evaluated.
NeuroBiometric: An Eye Blink Based Biometric Authentication System Using an Event-Based Neuromorphic Vision Sensor
Guang Chen*, Fa Wang, Xiaoding Yuan, Zhijun Li, Zichen Liang, Alois Knoll
IEEE/CAA Journal of Automatica Sinica
code / video / bibtex
@ARTICLE{guang-biometrics,
  author={G. {Chen} and F. {Wang} and X. {Yuan} and Z. {Li} and Z. {Liang} and A. {Knoll}},
  journal={IEEE/CAA Journal of Automatica Sinica}, 
  title={NeuroBiometric: An eye blink based biometric authentication system using an event-based neuromorphic vision sensor}, 
  year={2021},
  volume={8},
  number={1},
  pages={206-218},
  doi={10.1109/JAS.2020.1003483}}
    
Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data. This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution.
Globally Optimal Vertical Direction Estimation in Atlanta World
Yinlong Liu, Guang Chen*, Alois Knoll
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
code / video / bibtex
@article{liu2020globally,
  title={Globally optimal vertical direction estimation in Atlanta World},
  author={Liu, Yinlong and Chen, Guang and Knoll, Alois},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2020},
  publisher={IEEE}
}
    
In this paper, we propose a novel method for estimating the vertical direction in Atlanta world. It obtains the globally optimal solution by applying a BnB algorithm, without requiring any prior knowledge of the number of frames.
Multi-Objective Scheduling Strategy with Genetic Algorithm and Time Enhanced A* Planning for Autonomous Parking Robotics in High-Density Unmanned Parking Lots
Guang Chen, Jin Hou, Jinhu Dong, Zhijun Li, Shangding Gu, Bo Zhang, Junwei Yu, Alois Knoll
IEEE/ASME Transactions on Mechatronics, 2020
code / video / bibtex
@article{chen2020multi,
  title={Multi-Objective Scheduling Strategy with Genetic Algorithm and Time Enhanced A* Planning for Autonomous Parking Robotics in High-Density Unmanned Parking Lots},
  author={Chen, Guang and Hou, Jing and Dong, Jinhu and Li, Zhijun and Gu, Shangding and Zhang, Bo and Yu, Junwei and Knoll, Alois},
  journal={IEEE/ASME Transactions on Mechatronics},
  year={2020},
  publisher={IEEE}
}
    
This paper provides an efficient and convenient scheduling solution for the implementation of the high-density unmanned parking lot.
A Novel Illumination-Robust Hand Gesture Recognition System with Event-based Neuromorphic Vision Sensor
Guang Chen, Zhongcong Xu, Zhijun Li, Huajin Tang, Sanqing Qu, Kejia Ren, Alois Knoll
IEEE/ASME Transactions on Automation Science and Engineering, 2020
code / video / bibtex
@article{chen2021novel,
  title={A novel illumination-robust hand gesture recognition system with event-based neuromorphic vision sensor},
  author={Chen, Guang and Xu, Zhongcong and Li, Zhijun and Tang, Huajin and Qu, Sanqing and Ren, Kejia and Knoll, Alois},
  journal={IEEE Transactions on Automation Science and Engineering},
  volume={18},
  number={2},
  pages={508--520},
  year={2021},
  publisher={IEEE}
}
    
We propose an event-based gesture recognition system to overcome the detriment constraints and enhance the robustness of the recognition performance.
A Survey of the Four Pillars for Small Object Detection: Multiscale Representation, Contextual Information, Super-Resolution, and Region Proposal
Guang Chen, Haitao Wang, Kai Chen, Zhijun Li, Zida Song, Yinlong Liu, Wenkai Chen, Alois Knoll
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020
link / bibtex
@article{chen2020survey,
  title={A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal},
  author={Chen, Guang and Wang, Haitao and Chen, Kai and Li, Zhijun and Song, Zida and Liu, Yinlong and Chen, Wenkai and Knoll, Alois},
  journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  year={2020},
  publisher={IEEE}
}
    
In this article, the first-ever survey of recent studies in deep learning-based small object detection is presented.
Event-based Neuromorphic Vision for Autonomous Driving: A paradigm shift for bio-inspired visual sensing and perception
Guang Chen*, Hu Cao, Jorg Conradt, Huajin Tang, Florian Rohrbein, Alois Knoll
IEEE Signal Processing Magazine, 2020
video / bibtex
@article{chen2020event,
  title={Event-based neuromorphic vision for autonomous driving: a paradigm shift for bio-inspired visual sensing and perception},
  author={Chen, Guang and Cao, Hu and Conradt, Jorg and Tang, Huajin and Rohrbein, Florian and Knoll, Alois},
  journal={IEEE Signal Processing Magazine},
  volume={37},
  number={4},
  pages={34--49},
  year={2020},
  publisher={IEEE}
}
    
This article serves as a starting point for new research-ers and engineers in the autonomous driving field and provide a bird’s-eye view to both neuromorphic vision and autonomous driving research communities.
NeuroAED: Towards Efficient Abnormal Event Detection in Visual Surveillance With Neuromorphic Vision Sensor
Guang Chen*, Peigen Liu, Zhengfa Liu, Huajin Tang, Lin Hong, Jinhu Dong, Jorg Conradt, Alois Knoll
IEEE Transactions on Information Forensics and Security, 2020
dataset / code / bibtex
@article{chen2020neuroaed,
  title={Neuroaed: Towards efficient abnormal event detection in visual surveillance with neuromorphic vision sensor},
  author={Chen, Guang and Liu, Peigen and Liu, Zhengfa and Tang, Huajin and Hong, Lin and Dong, Jinhu and Conradt, J{\"o}rg and Knoll, Alois},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={16},
  pages={923--936},
  year={2020},
  publisher={IEEE}
}
    
Existing methods usually rely on standard frame-based cameras to record the data and process them with computer vision technologies. In contrast, this paper presents a novel neuromorphic vision based abnormal event detection system.
EDDD: Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor
Guang Chen*, Lin Hong, Jinhu Dong, Peigen Liu, Jorg Conradt, Alois Knoll
IEEE Sensors Journal, 2020
dataset / code / bibtex
@ARTICLE{Guang:eddd2020,
  author={G. {Chen} and L. {Hong} and J. {Dong} and P. {Liu} and J. {Conradt} and A. {Knoll}},
  journal={IEEE Sensors Journal}, 
  title={EDDD: Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor}, 
  year={2020},
  volume={20},
  number={11},
  pages={6170-6181},
  doi={10.1109/JSEN.2020.2973049}}
    
The first investigation of the usage of DVS in drowsiness driving detection applications.
A Novel Visible Light Positioning System With Event-Based Neuromorphic Vision Sensor
Guang Chen*, Wenkai Chen, Qianyi Yang, Zhongcong Xu, Longyu Yang, Jorg Conradt, Alois Knoll
IEEE Sensors Journal, 2020
dataset / code / bibtex
@ARTICLE{Guang:vlp,
  author={G. {Chen} and W. {Chen} and Q. {Yang} and Z. {Xu} and L. {Yang} and J. {Conradt} and A. {Knoll}},
  journal={IEEE Sensors Journal}, 
  title={A Novel Visible Light Positioning System With Event-Based Neuromorphic Vision Sensor}, 
  year={2020},
  volume={20},
  number={17},
  pages={10211-10219},
  doi={10.1109/JSEN.2020.2990752}}
    
In our work, a novel VLP system using an event-based neuromorphic vision sensor (event camera) as the light receiver is proposed.
Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor
Xiangyong Liu, Guang Chen, Xuesong Sun, Alois Knoll
IEEE Internet of Things Journal, 2020
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
This article proposes to utilize neuromorphic vision sensor (DVS) for detecting the moving objects and estimating their movement states.
Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors
Guang Chen*, Hu Cao, Canbo Ye, Zhenyang Zhang, Xingbo Liu, Xuhui Mo, Zhongnan Qu, Jorg Conradt, Florian Rohrbein, Alois Knoll
Frontiers in Neurorobotics, 2019
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies.
Deep anticipation: lightweight intelligent mobile sensing for unmanned vehicles in IoT by recurrent architecture
Guang Chen*, Shu Liu, Kejia Ren, Zhongnan Qu, Changhong Fu, Geren Hinz, Alois Knoll
IET Intelligent Transport System, 2019
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
In this article, we model the communication challenge as latency, packet delay variation and measurement noise which severely deteriorate the reliability and quality of IoT data.
FLGR: Fixed Length Gists Representation Learning for RNN-HMM Hybrid-Based Neuromorphic Continuous Gesture Recognition
Guang Chen*, Jieneng Chen, Marten Lienen, Jorg Conradt, Florian Rohrbein, Alois Knoll
Frontiers in Neuroscience, 2019
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
In this study, a novel representation learning method was proposed: Fixed Length Gists Representation (FLGR) learning for event-based gesture recognition.
Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection
Shu Miao, Guang Chen*, Xiangyu Ning, Yang Zi, Kejia Ren Zhenshan Bing, Alois Knoll
Frontiers in Neurorobotics, 2019
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
In this data report, we introduce three new neuromorphic vision datasets recorded by a novel neuromorphic vision sensor named Dynamic Vision Sensors (DVS).
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
Yinlong Liu, Xuechen Li, Manning Wang, Alois Knoll, Guang Chen, Zhijian Song
Remote Sensing, 2019
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
In this paper, we consider pairwise constraints and propose a novel algorithm utilizing global optimization method Branch-and-Bound (BnB) for solving the absolute pose estimation problem.
Correlation Filter-Based Visual Tracking for UAV with Online Multi-Feature Learning
Changhong Fu, Fuling Lin, Yiming Li, Guang Chen
Remote Sensing, 2019
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
In this paper, a novel online learning-based tracker is presented for the unmanned aerial vehicle (UAV) in different types of tracking applications, such as pedestrian following, automotive chasing, and building inspection.
Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle
Zhenshan Bing, Claus Meschede, Guang Chen, Alois Knoll, Kai Huang
Remote Sensing, 2019
dataset / code / bibtex
@ARTICLE{Guang:iot,
  author={X. {Liu} and G. {Chen} and X. {Sun} and A. {Knoll}},
  journal={IEEE Internet of Things Journal}, 
  title={Ground Moving Vehicle Detection and Movement Tracking Based on the Neuromorphic Vision Sensor}, 
  year={2020},
  volume={7},
  number={9},
  pages={9026-9039},
  doi={10.1109/JIOT.2020.3001167}}
    
In this paper, we introduce both indirect and direct end-to-end training methods of SNNs for a lane-keeping vehicle.
Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform
Guang Chen, Zhenshan Bing, Florian Rohrbein, Jorg Conradt, Kai Huang, Long Cheng, Zhuangyi Jiang, Alois Knoll
IEEE Transactions on Cognitive and Developmental Systems, 2017
dataset / code / bibtex
@ARTICLE{Guang:tcdssnake,
  author={G. {Chen} and Z. {Bing} and F. {Röhrbein} and J. {Conradt} and K. {Huang} and L. {Cheng} and Z. {Jiang} and A. {Knoll}},
  journal={IEEE Transactions on Cognitive and Developmental Systems}, 
  title={Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform}, 
  year={2019},
  volume={11},
  number={1},
  pages={1-12},
  doi={10.1109/TCDS.2017.2712712}}
    
We present a hybrid neuromorphic computing paradigm to bridge this gap by combining the neurorobotics platform (NRP) with the neuromorphic snake-like robot (NeuroSnake).
Combining unsupervised learning and discrimination for 3D action recognition
Guang Chen, Daniel Clarke, Manuel Giuliani, Andre Gaschler, Alois Knoll
Signal Processing, 2015
dataset / code / bibtex
@ARTICLE{Guang:tcdssnake,
  author={G. {Chen} and Z. {Bing} and F. {Röhrbein} and J. {Conradt} and K. {Huang} and L. {Cheng} and Z. {Jiang} and A. {Knoll}},
  journal={IEEE Transactions on Cognitive and Developmental Systems}, 
  title={Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform}, 
  year={2019},
  volume={11},
  number={1},
  pages={1-12},
  doi={10.1109/TCDS.2017.2712712}}
    
We propose an ensemble approach using a discriminative learning algorithm, where each base learner is a discriminative multi-kernel-learning classifier, trained to learn an optimal combination of joint-based features.

I like this website and this