SciTePress - Affiliation Profile

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Select a Subject
Refine Ontology Subject

Eindhoven University of Techology

Context (please use complete words only)

Field

Sort by

123 Papers

Automated Generation of Instance Segmentation Labels for Traffic Surveillance Models

Topics: Segmentation and Grouping; Features Extraction; Object Detection and Localization; Domain Adaptation

Authors: D. Scholte ¹ , T. T. G. Urselmann ² , M. H. Zwemer ^{2

;

1} , E. Bondarev ² and P. H. N. de With ²

Affiliations: ¹ ViNotion BV, Eindhoven, The Netherlands , ² Department of Electrical Engineering, Eindhoven University, Eindhoven, The Netherlands

Digital Object Identifier: 10.5220/0012319500003660

Proceedings of VISAPP 2024, ISBN: 978-989-758-679-8

Quick Abstract

This paper focuses on instance segmentation and object detection for real-time traffic surveillance applications. Although instance segmentation is currently a hot topic in literature, no suitable dataset for traffic surveillance applications is publicly available and limited work is available with real-time performance. A custom proprietary dataset is available for training, but it contains only bounding-box annotations and lacks segmentation annotations. The paper explores methods for automated generation of instance segmentation labels for custom datasets that can be utilized to finetune state-of-the-art segmentation models to specific application domains. Real-time performance is obtained by adopting the recent YOLACT instance segmentation with the YOLOv7 backbone. Nevertheless, it requires modification of the loss function and an implementation of ground-truth matching to overcome handling imperfect instance labels in custom datasets. Experiments show that it is possible to achieve a high instance segmentation performance using a semi-automatically generated dataset, especially when using the Segment Anything Model for generating the labels.

Robust Path Planning in the Wild for Automatic Look-Ahead Camera Control

Topics: Segmentation and Grouping; Vision for Robotics; Deep Learning for Visual Understanding ; Transfer Learning

Authors: Sander Klomp ^{1

;

2} and Peter H. N. de With ¹

Affiliations: ¹ Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands , ² ViNotion B.V., Eindhoven, The Netherlands

Digital Object Identifier: 10.5220/0011614200003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

Finding potential driving paths on unstructured roads is a challenging problem for autonomous driving and robotics applications. Although the rise of autonomous driving has resulted in massive public datasets, most of these datasets focus on urban environments and feature almost exclusively paved roads. To circumvent the problem of limited public datasets of unpaved roads, we combine seven public vehicle-mounted-camera datasets with a very small private dataset and train a neural network to achieve accurate road segmentation on almost any type of road. This trained network vastly outperforms networks trained on individual datasets when validated on our unpaved road datasets, with only a minor performance reduction on the highly challenging public WildDash dataset, which is mostly urban. Finally, we develop an algorithm to robustly transform these road segmentations to road centerlines, used to automatically control a vehicle-mounted PTZ camera.

The Effect of Covariate Shift and Network Training on Out-of-Distribution Detection

Topics: Machine Learning Technologies for Vision; Features Extraction; Deep Learning for Visual Understanding

Authors: Simon Mariani ¹ , Sander Klomp ² , Rob Romijnders ¹ and Peter H. N. de With ²

Affiliations: ¹ University of Amsterdam, Amsterdam, The Netherlands , ² Eindhoven University of Technology, Eindhoven, The Netherlands

Digital Object Identifier: 10.5220/0011725900003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

The field of Out-of-Distribution (OOD) detection aims to separate OOD data from in-distribution (ID) data in order to make safe predictions. With the increasing application of Convolutional Neural Networks (CNNs) in sensitive environments such as autonomous driving and security, this field is bound to become indispensable in the future. Although the OOD detection field has made some progress in recent years, a fundamental understanding of the underlying phenomena enabling the separation of datasets remains lacking. We find that the OOD detection relies heavily on the covariate shift of the data and not so much on the semantic shift, i.e. a CNN does not carry explicit semantic information and relies solely on differences in features. Although these features can be affected by the underlying semantics, this relation does not seem strong enough to rely on. Conversely, we found that since the CNN training setup determines what features are learned, that it is an important factor for the OOD performance. We found that variations in the model training can lead to an increase or decrease in the OOD detection performance. Through this insight, we obtain an increase in OOD detection performance on the common OOD detection benchmarks by changing the training procedure and using the simple Maximum Softmax Probability (MSP) model introduced by (Hendrycks and Gimpel, 2016). We hope to inspire others to look more closely into the fundamental principles underlying the separation of two datasets. The code for reproducing our results can be found at https://github.com/SimonMariani/OOD- detection.

3D Detection of Vehicles from 2D Images in Traffic Surveillance

Topics: Object Detection and Localization; Video Surveillance and Event Detection; Domain Adaptation

Authors: M. H. Zwemer ^{1

;

2} , D. Scholte ^{1

;

2} , R. G. J. Wijnhoven ² and P. H. N. de With ¹

Affiliations: ¹ Department of Electrical Engineering, Eindhoven University, Eindhoven, The Netherlands , ² ViNotion BV, Eindhoven, The Netherlands

Digital Object Identifier: 10.5220/0010783600003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick Abstract

Traffic surveillance systems use monocular cameras and automatic visual algorithms to locate and observe traffic movement. Object detection results in 2D object boxes around vehicles, which relate to inaccurate real-world locations. In this paper, we employ the existing KM3D CNN-based 3D detection model, which directly estimates 3D boxes around vehicles in the camera image. However, the KM3D model has only been applied earlier in autonomous driving use cases with different camera viewpoints. However, 3D annotation datasets are not available for traffic surveillance, requiring the construction of a new dataset for training the 3D detector. We propose and validate four different annotation configurations that generate 3D box annotations using only camera calibration, scene information (static vanishing points) and existing 2D annotations. Our novel Simple box method does not require segmentation of vehicles and provides a more simple 3D box construction, which assumes a fixed predefined vehicle width. The Simple box pipeline provides the best 3D object detection results, resulting in 51.9% AP3D using KM3D trained on this data. The 3D object detector can estimate an accurate 3D box up to a distance of 125 meters from the camera, with a median middle point mean error of only 0.5-1.0 meter.

Critical Vehicle Detection for Intelligent Transportation Systems

Topics: Vision and Image Processing; Congestion Management and Avoidance; Intelligent Infrastructure and Guidance Systems; Air, Road, and Rail Traffic Management; Road Safety and Transport Security

Authors: Erkut Akdag , Egor Bondarev and Peter H. N. De With

Affiliation: Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5612AZ, The Netherlands

Digital Object Identifier: 10.5220/0010968900003191

Proceedings of VEHITS 2022, ISBN: 978-989-758-573-9

Quick Abstract

An intelligent transportation system (ITS) is one of the core elements of smart cities, enhancing public safety and relieving traffic congestion. Detection and classification of critical vehicles, such as police cars and ambulances, passing through roadways form crucial use cases for ITS. This paper proposes a solution for detecting and classifying safety-critical vehicles on urban roadways using deep learning models. At present, a large-scale dataset for critical vehicles is not publicly available. The appearance scarcity of emergency vehicles and different coloring standards in various countries are significant challenges. To cope with the mentioned drawbacks and to address the unique requirements of our smart city project, we first generate a large-scale critical vehicle dataset, combining images retrieved from various sources with the support of the YOLO vehicle detection model. The classes of the generated dataset are: fire truck, police car, ambulance, military police car, dangerous truck, and standard vehicle. Second, we compare the performance of the Vision in Transformer (ViT) network against the traditional convolutional neural networks (CNNs) for the task of critical vehicle classification. Experimental results on our dataset reveal that the ViT-based solution reaches an average accuracy and recall of 99.39% and 99.34%, respectively.

Video-based Detection and Tracking with Improved Re-Identification Association for Pigs and Laying Hens in Farms

Topics: Image and Video Coding and Compression; Machine Learning Technologies for Vision; Features Extraction; Object Detection and Localization; Video Surveillance and Event Detection

Authors: Qinghua Guo ¹ , Yue Sun ¹ , Lan Min ¹ , Arjen van Putten ² , Egbert Frank Knol ³ , Bram Visser ⁴ , T. Bas Rodenburg ² , J. Elizabeth Bolhuis ⁵ , Piter Bijma ⁵ and Peter H. N. de With ¹

Affiliations: ¹ Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands , ² Department of Animals in Science and Society, Utrecht University, Utrecht, The Netherlands , ³ Topigs Norsvin Research Center, Beuningen, The Netherlands , ⁴ Hendrix Genetics, Boxmeer, The Netherlands , ⁵ Department of Animal Sciences, Wageningen University & Research, Wageningen, The Netherlands

Digital Object Identifier: 10.5220/0010788100003124

Proceedings of VISAPP 2022/VISIGRAPP, ISBN: 978-989-758-555-5

Quick Abstract

It is important to detect negative behavior of animals for breeding in order to improve their health and welfare. In this work, AI is employed to assist individual animal detection and tracking, which enables the future analysis of behavior for individual animals. The study involves animal groups of pigs and laying hens. First, two state-of-the-art deep learning-based Multi-Object Tracking (MOT) methods are investigated, namely Joint Detection and Embedding (JDE) and FairMOT. Both models detect and track individual animals automatically and continuously. Second, a weighted association algorithm is proposed, which is feasible for both MOT methods to optimize the object re-identification (re-ID), thereby improving the tracking performance. The proposed methods are evaluated on manually annotated datasets. The best tracking performance on pigs is obtained by FairMOT with the weighted association, resulting in an IDF1 of 90.3%, MOTA of 90.8%, MOTP of 83.7%, number of identity switches of 14, and an execution rate of 20.48 fps. For the laying hens, FairMOT with the weighted association also achieves the best tracking performance, with an IDF1 of 88.8%, MOTA of 86.8%, MOTP of 72.8%, number of identity switches of 2, and an execution rate of 21.01 fps. These results show a promising high accuracy and robustness for the individual animal tracking.

Automated Infant Monitoring based on R-CNN and HMM

Topics: Machine Learning Technologies for Vision; Face and Expression Recognition; Video Surveillance and Event Detection; Assistive Computer Vision

Authors: Cheng Li ¹ , A. Pourtaherian ¹ , L. van Onzenoort ² and P. H. N. de With ¹

Affiliations: ¹ Eindhoven University of Technology, Eindhoven, The Netherlands , ² Maxima Medical Center, Veldhoven, The Netherlands

Digital Object Identifier: 10.5220/0010299605530560

Proceedings of VISAPP 2021/VISIGRAPP, ISBN: 978-989-758-488-6

Quick Abstract

Manual monitoring of young infants suffering from reflux is a significant effort, since infants can hardly articulate their feelings. This work proposes a near real-time video-based infant monitoring system for the analysis of infant expressions. The discomfort moments can be correlated with a reflux measurement for gastroesophageal reflux disease diagnose. The system consists of two components: expression classification and expression state stabilization. The expression classification is realized by Faster R-CNN and the state stabilization is implemented with a Hidden Markov Model. The experimental results show a mean average precision of 82.3% and 83.4% for 7 different expression classifications, and up to 90% for discomfort detection, evaluated with both clinical and daily datasets. Moreover, when adopting temporal analysis, the false expression changes between frames can be reduced up to 65%, which significantly enhances the consistency of the system output.

Comparing Classifiers’ Performance under Differential Privacy

Topics: Personal Data Protection for Information Systems; Data Protection; Privacy; Machine Learning and AI Security

Authors: Milan Lopuhaä-Zwakenberg , Mina Alishahi , Jeroen Kivits , Jordi Klarenbeek , Gert-Jan van der Velde and Nicola Zannone

Affiliation: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

Digital Object Identifier: 10.5220/0010519000500061

Proceedings of SECRYPT 2021, ISBN: 978-989-758-524-1

Quick Abstract

The application of differential privacy in privacy-preserving data analysis has gained momentum in recent years. In particular, it provides an effective solution for the construction of privacy-preserving classifiers, in which one party owns the data and another party is interested in obtaining a classifier model from this data. While several approaches have been proposed in the literature to employ differential privacy for the construction of classifiers, an understanding of the difference in performance of these classifiers is currently missing. This knowledge enables the data owner and the analyst to select the most appropriate classification algorithm and training parameters in order to guarantee high privacy requirements while minimizing the loss of accuracy. In this study, we investigate the impact of the use of differential privacy on three well-known classifiers, i.e., Naïve Bayes, SVM, and Decision Tree classifiers. To this end, we show how these classifiers can be trained in a differential privacy setting and perform extensive experiments to evaluate the effect of this privacy enforcement on their performance.

Dual CNN-based Face Tracking Algorithm for an Automated Infant Monitoring System

Topic: Deep Learning for Tracking

Authors: Cheng Li , Genyu Song , A. Pourtaherian and P. H. N. de With

Affiliation: Eindhoven University of Technology, Eindhoven, The Netherlands

Digital Object Identifier: 10.5220/0010384308810887

Proceedings of VISAPP 2021/VISIGRAPP, ISBN: 978-989-758-488-6

Quick Abstract

Face tracking is important for designing a surveillance system when facial features are used as main descriptors. In this paper, we propose an on-line updating face tracking method, which is not only suitable for specific tasks, such as infant monitoring, but also a generic human-machine interaction application where face recognition is required. The tracking method is based on combining the architecture of the GOTURN and YOLO tiny face detector, which enables the tracking model to be updated over time. Tracking of objects is realized by analyzing two neighboring frames through a deep neural network. On-line updating is achieved by comparing the tracking result and face detection obtained from the YOLO tiny face detector. The experimental results have shown that our proposed tracker achieves an AUC of 97.9% for precision plot and an AUC of 91.8% for success plot, which outperforms other state-of-the-art tracking methods when used in the infant monitoring application.

Energy-based Control for Soft Manipulators using Cosserat-beam Models

Topics: Modeling, Simulation and Architecture; System Modeling; Systems Modeling and Simulation; Systems Modeling and Simulation

Authors: Brandon Caasenbrood ¹ , Alexander Pogromsky ^{2

;

1} and Henk Nijmeijer ¹

Affiliations: ¹ Department of Mechanical Engineering, Eindhoven University of Technology, The Netherlands , ² Department of Control Systems and Informatics, Saint-Petersburg National Research University of Information Technologies, Mechanics, and Optics (ITMO), Russian Federation

Digital Object Identifier: 10.5220/0010581503110319

Proceedings of ICINCO 2021, ISBN: 978-989-758-522-7

Quick Abstract

In this work, we describe an energy-based control method for under-actuated soft manipulators. The continuous dynamics of the soft robot are modeled by the differential geometry of Cosserat beams. Through a finite-dimensional truncation, a reduced port-Hamiltonian model is obtained that preserves desirable passivity conditions. Exploiting the passivity, we propose a stabilizing energy-shaping controller that ensures the potential energy is minimal at the desired end-effector configuration. Finally, the effectiveness of the energy-based controller is demonstrated through simulations of a soft manipulator inspired by the tentacle of an octopus.