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A driver-in-the-loop co-simulation framework for testing predictive EDAS for commercial vehicles in urban environments

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A driver-in-the-loop co-simulation framework for testing pre...

6th International Commercial Vehicle Technology Symposium, CVT 2020

作者： Chada, Sai Krishna Gőrges, Daniel Ebert, Achim Teutsch, Roman Division of Electromobility Department of Electrical and Computer Engineering University of Kaiserslautern Kaiserslautern67663 Germany Computer Graphics and HCI Group Department of Computer Science University of Kaiserslautern Kaiserslautern67663 Germany Institute for Mechanical and Automotive Engineering Department of Mechanical and Process Engineering University of Kaiserslautern Kaiserslautern67663 Germany

ISBN: (纸本)9783658297169

To experience realistic driving scenarios in the virtual simulation environment, strengths of both the traffic simulation and the vehicle simulation tools must be combined. In this paper, a driver-in-the-loop co-simulation framework for testing predictive eco-driving assistance systems (EDAS) for commercial vehicles in urban environments is proposed. The co-simulation is performed by exchanging information between IPG TruckMaker, a vehicle simulation software tailored for commercial vehicles, and SUMO, an open-source software for microscopic traffic simulation. To avoid delays during the data exchange process, time-steps of both the tools are synchronized. The co-simulation environment replicates a bus route in the city of Kaiserslautern along with the corresponding bus stops, traffic lights, road speed limits, pedestrian crossings and road elevation. The stochastic traffic and pedestrian flow in the road network is generated using SUMO. In this work, the co-simulation framework is extended by introducing the driver into the simulation loop. This is achieved by coupling the vehicle simulation tool with a driving simulator, in which the driver controls the ego-vehicle. The proposed framework will serve to test various use-cases of predictive eco-driving assistant systems (EDAS). © Der/die Autor(en), exklusiv lizenziert durch.

关键词： Open source software

Bridging the Gap with Sensor Networks and Cloud Computing in the Geospatial Web for Optimized Data Services 7

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Bridging the Gap with Sensor Networks and Cloud Computing in...

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Veeresh, G. Sreevani, N. Lakhanpal, Sorabh Sharma, Saurabh Reddy, B.Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Bangalore India Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

Implementing cloud computing and data networks for local internet services is a priority. This research addresses the issue by introducing a novel approach to enhance the quality of regional internet data services. The proposed technique, Geographical Data Fusion with Dynamic Cloud Resource Allocation (GDF-DCRA), enhances map analytics by combining geographical data fusion with dynamic cloud resource allocation. GDF-DCRA enhances the speed of geospatial analytics by reducing sensor data, efficiently allocating cloud resources, and integrating spatial and temporal data. It consistently outperforms six industry benchmarks in terms of speed, latency, scalability, data accuracy, bandwidth efficiency, fault tolerance, and resource utilisation. Despite the increasing difficulty of working with real-time location data, the proposed approach has the potential to address these challenges. © 2024 IEEE.

关键词： Spatio-temporal data

Majority Voting-based Consensus Mechanism for UAVs Decision Making in Battlefield

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Majority Voting-based Consensus Mechanism for UAVs Decision ...

International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Riya Kakkar Rajesh Gupta Mohammad S. Obaidiat Nilesh Kumar Jadav Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan Jordan School of Computer and Communication Engineering University of Science and Technology Beijing China The Amity University Noida Uttar Pradesh India

The revolutionary and rapid development of Information and Communication Technology (ICT) has significantly contributed to modernizing military operations by improving the long-range communication and information sharing of unmanned aerial vehicles (UAVs) on the battlefield. However, efficient decision-making between clusters of UAVs controlled by the controller station (CS) is still one of the critical aspects that can cause potential threats to the armed forces on the battlefield. Thus, we have proposed a majority voting-based mechanism to attain consensus decision-making for UAVs, i.e., followers, monitored by the CS (leader) on the battlefield. Furthermore, we have focused on various voting scenarios to choose the efficient decision (attack or retreat) for the armed forces without any conflict between leader and follower utilizing the majority voting mechanism. Finally, the performance evaluation of the proposed mechanism is analyzed and simulated using the performance metrics of consensus delay and throughput.

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Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations through Activation Map Variation Analyses in Face Recognition Models

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arXiv 2021年

作者： Fu, Biying Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

It is challenging to derive explainability for unsupervised or statistical-based face image quality assessment (FIQA) methods. In this work, we propose a novel set of explainability tools to derive reasoning for different FIQA decisions and their face recognition (FR) performance implications. We avoid limiting the deployment of our tools to certain FIQA methods by basing our analyses on the behavior of FR models when processing samples with different FIQA decisions. This leads to explainability tools that can be applied for any FIQA method with any CNN-based FR solution using activation mapping to exhibit the network’s activation derived from the face embedding. To avoid the low discrimination between the general spatial activation mapping of low and high-quality images in FR models, we build our explainability tools in a higher derivative space by analyzing the variation of the FR activation maps of image sets with different quality decisions. We demonstrate our tools and analyze the findings on four FIQA methods, by presenting inter and intra-FIQA method analyses. Our proposed tools and the analyses based on them point out, among other conclusions, that high-quality images typically cause consistent low activation on the areas outside of the central face region, while low-quality images, despite general low activation, have high variations of activation in such areas. Our explainability tools also extend to analyzing single images where we show that low-quality images tend to have an FR model spatial activation that strongly differs from what is expected from a high-quality image where this difference also tends to appear more in areas outside of the central face region and does correspond to issues like extreme poses and facial occlusions. The implementation of the proposed tools is accessible here. © 2021, CC BY.

关键词： Face recognition

Mask-invariant Face Recognition through Template-level Knowledge Distillation

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arXiv 2021年

作者： Huber, Marco Boutros, Fadi Kirchbuchner, Florian Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

The emergence of the global COVID-19 pandemic poses new challenges for biometrics. Not only are contactless biometric identification options becoming more important, but face recognition has also recently been confronted with the frequent wearing of masks. These masks affect the performance of previous face recognition systems, as they hide important identity information. In this paper, we propose a mask-invariant face recognition solution (MaskInv) that utilizes template-level knowledge distillation within a training paradigm that aims at producing embeddings of masked faces that are similar to those of non-masked faces of the same identities. In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss, ElasticFace, using masked and non-masked faces. In a step-wise ablation study on two real masked face databases and five mainstream databases with synthetic masks, we prove the rationalization of our MaskInv approach. Our proposed solution outperforms previous state-of-the-art (SOTA) academic solutions in the recent MFRC-21 challenge in both scenarios, masked vs masked and masked vs non-masked, and also outperforms the previous solution on the MFR2 dataset. Furthermore, we demonstrate that the proposed model can still perform well on unmasked faces with only a minor loss in verification performance. The code, the trained models, as well as the evaluation protocol on the synthetically masked data are publicly available: https://***/ fdbtrs/Masked-Face-Recognition-KD. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

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arXiv 2021年

作者： Boutros, Fadi Fang, Meiling Klemt, Marcel Fu, Biying Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. 1 Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

Partial attack supervision and regional weighted inference for masked face presentation attack detection

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arXiv 2021年

作者： Fang, Meiling Boutros, Fadi Kuijper, Arjan Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARSCoV-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance by varying the focus on different facial areas. Our proposed method is not directly linked to specific network architecture and thus can be directly incorporated into any common or custom-designed network. In our work, two neural networks (DeepPixBis [21] and MixFaceNet [4]) are selected as backbones. The experiments are demonstrated on the collaborative real mask attack (CRMA) database [17]. Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces. Moreover, we present a detailed step-wise ablation study pointing out the individual and joint benefits of the proposed concepts on the overall PAD performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Facings

The effect of wearing a face mask on face image quality

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arXiv 2021年

作者： Fu, Biying Kirchbuchner, Florian Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Due to the COVID-19 situation, face masks have become a main part of our daily life. Wearing mouth-and-nose protection has been made a mandate in many public places, to prevent the spread of the COVID-19 virus. However, face masks affect the performance of face recognition, since a large area of the face is covered. The effect of wearing a face mask on the different components of the face recognition system in a collaborative environment is a problem that is still to be fully studied. This work studies, for the first time, the effect of wearing a face mask on face image quality by utilising state-of-theart face image quality assessment methods of different natures. This aims at providing better understanding on the effect of face masks on the operation of face recognition as a whole system. In addition, we further studied the effect of simulated masks on face image utility in comparison to real face masks. We discuss the correlation between the mask effect on face image quality and that on the face verification performance by automatic systems and human experts, indicating a consistent trend between both factors. The evaluation is conducted on the database containing (1) no-masked faces, (2) real face masks, and (3) simulated face masks, by synthetically generating digital facial masks on no-masked faces. Finally, a visual interpretation of the face areas contributing to the quality score of a selected set of quality assessment methods is provided to give a deeper insight into the difference of network decisions in masked and non-masked faces, among other variations. Copyright © 2021, The Authors. All rights reserved.

关键词： Viruses

Removing fences from sweep motion videos using global 3D reconstruction and fence-aware light field rendering

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Computational Visual Media 2019年第1期5卷 21-32页

作者： Chanya Lueangwattana Shohei Mori Hideo Saito Department of Science and Technology Keio University Institute of Computer Graphics and Vision Graz University of Technology

Diminishing the appearance of a fence in an image is a challenging research area due to the characteristics of fences(thinness, lack of texture, etc.) and the need for occluded background restoration. In this paper, we describe a fence removal method for an image sequence captured by a user making a sweep motion, in which occluded background is potentially observed. To make use of geometric and appearance information such as consecutive images, we use two well-known approaches: structure from motion and light field rendering. Results using real image sequences show that our method can stably segment fences and preserve background details for various fence and background combinations. A new video without the fence, with frame coherence, can be successfully provided.

关键词： video fence video repair diminished reality(DR) structure from motion(SfM) light field rendering(LFR)