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IEEE Transactions on Intelligent Vehicles 2024年 1-19页

作者： Cao, Yue Shangguan, Wei Visser, Arnoud Chen, Junjie Chai, Linguo Cai, Baigen School of Automation and Intelligence Beijing Jiaotong University Beijing China School of Automation and Intelligence and State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Intelligent Robotics and Computer Vision Lab of the Informatics Institute Faculty of Science University of Amsterdam The Netherlands

Detecting surrounding situations and reacting accordingly to avoid collisions remains a challenging task for autonomous driving. This task requires predicting the trajectories of surrounding agents and assessing the potential risk of future situations, which can be difficult to achieve solely through onboard vehicle devices. Therefore, this paper proposes a cooperative architecture for trajectory prediction and risk assessment conducted on roadside devices (RSUs) to assist Connected and Autonomous Vehicles (CAVs). Firstly, we develop a segmentbased prediction model (SegNet) tailored to hub signalized intersections. Intersections are divided into multiple segments, and the Curvilinear coordinates are utilized to indicate the geometric road features. The model leverages individual interaction cues in the ego segment and group features in the merging segments, while also incorporating traffic signal information to generate multimodal prediction results. In terms of risk assessment, we utilize the prediction results to provide hierarchical assistance, such as risk values, risk maps, and reference trajectories. Offline experimental results demonstrate that our SegNet model achieves competitive and well-balanced performance compared to stateof-the-art methods on the CitySim Database, with more accurate and smooth prediction trajectories. Through real-time CARLA and SUMO co-simulation, the performance of assisted CAVs indicates that they can safely and effectively navigate with the support of the proposed architecture. IEEE

关键词： Real time systems

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*** 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

arXiv

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arXiv 2023年

作者： Raji, Ayoub Caporale, Danilo Gatti, Francesco Giove, Andrea Verucchi, Micaela Malatesta, Davide Musiu, Nicola Toschi, Alessandro Popitanu, Silviu Roberto Bagni, Fabio Bosi, Massimiliano Liniger, Alexander Bertogna, Marko Morra, Daniele Amerotti, Francesco Bartoli, Luca Martello, Federico Porta, Riccardo University of Modena and Reggio Emilia Italy University of Parma Italy Technology Innovation Institute - Autonomous Robotics Research Center United Arab Emirates HIPERT srl Italy University of Pisa Italy Computer Vision Lab ETH Zurich Switzerland

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars. This paper presents the complete software architecture used by team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modelling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed respectively second and third. Copyright © 2023, The Authors. All rights reserved.

关键词： Racing automobiles

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P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

P1AC: Revisiting Absolute Pose From a Single Affine Correspo...

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International Conference on computer vision (ICCV)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group ETH Zürich

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/.

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Robotic Software System for the Disaster Circumstances: System of Team KAIST in the DARPA robotics Challenge Finals

Robotic Software System for the Disaster Circumstances: Syst...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jeongsoo Lim Inwook Shim Okkee Sim Hyunmin Joe Inhyeok Kim Jungho Lee Jun-Ho Oh Humanoid Research Center School of Mechanical Aerospace & Systems Engineering Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Robotics and Computer Vision Laboratory Department of Electrical Engineering College of Information Science and Technology Korea Advanced Institute of Science and Technology Rainbow Co.

ISBN: (纸本)9781479968862

We developed a software system for operating the humanoid robot DRC-HUBO+ in disaster circumstances that the US Defense Advanced Research Projects Agency suggested. This system was developed under the consideration of the stability of whole system, the systemic software environment for multiple developers, and the recovery routine when the system encounters a seriously abnormal situation. With these design goals, we devised our strategy for three domains: system, vision, and communication. Following the strategy we made two software frameworks (PODO and VPC) and one logical flow of data for remote process management. With this software system, we conducted all Tasks in the DARPA robotics Challenge Finals, and we won the competition.

关键词： Software systems DARPA design target robotics Disasters Religious Missions flow datas

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Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection

arXiv

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arXiv 2023年

作者： Liu, Yuyang Cong, Yang Goswami, Dipam Liu, Xialei van de Weijer, Joost State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China South China University of Technology China Computer Vision Center Barcelona Spain VCIP CS Nankai University China Department of Computer Science Universitat Autònoma de Barcelona Spain

In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model 1 © 2023, CC BY.

关键词： Object detection

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Group-wise inhibition based feature regularization for robust classification

arXiv

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arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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An Improved SSD for small target detection

An Improved SSD for small target detection

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作者： Xiang Li Haibo LuoX Key Laboratory of Opt-Electronic Information Processing Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences The Key Laboratory of Image Understanding and Computer Vision

SSD is one of heuristic one-stage target detection *** it has got impressive results in general target detection,it still struggles in small-size object detection and precise *** this paper,we proposed an improved SSD which forces on the small-size target *** include a shallow and high resolution feature into the hierarchical detection feature which are used for ***,we fuse the detection features(including the shallow and high resolution one) as a feature pyramid through some 1 × 1 convolution layers and unsample operations to pass information from deep features to the shallow ones,aiming to enrich the semantic information of the shallow *** make the network easier to converge,we add a L2 normalization to the bottom detection feature of the feature pyramid to make a norm balance between each pyramid *** experimental results on the VEDAI dataset show that the proposed method has obtained impressive progress than the original SSD for the small targets detection.

关键词： Convolution neural network Target detection SSD Small targets

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UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition

arXiv

引用

arXiv 2023年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Computer Vision Institute Shenzhen University China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China Alan Turing Institute United Kingdom SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace. © 2023, CC BY.

关键词： Face recognition

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Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains

arXiv

引用

arXiv 2020年

作者： Liu, Quande Dou, Qi Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong T Stone Robotics Institute Chinese University of Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. Importantly, considering the deficiencies encountered when applying a segmentation model to unseen domains (i.e., incomplete shape and ambiguous boundary of the prediction masks), we further introduce two complementary loss objectives to enhance the meta-optimization, by particularly encouraging the shape compactness and shape smoothness of the segmentations under simulated domain shift. We evaluate our method on prostate MRI data from six different institutions with distribution shifts acquired from public datasets. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains4 Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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