We are happy to announce that the Augmented Vision group will present 2 papers in the upcoming BMVC 2021 Conference, 22-25 November, 2021:

The British Machine Vision Conference (BMVC) is the British Machine Vision Association (BMVA) annual conference on machine vision, image processing, and pattern recognition. It is one of the major international conferences on computer vision and related areas held in the UK. With increasing popularity and quality, it has established itself as a prestigious event on the vision calendar. Homepage: https://www.bmvc2021.com/  

The 2 accepted papers are:

1.  TICaM: A Time-of-flight In-car Cabin Monitoring Dataset
Authors: Jigyasa Singh Katrolia, Ahmed Elsherif, Hartmut Feld, Bruno Mirbach, Jason Raphael Rambach, Didier Stricker

Summary: TICaM is a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. The dataset goes beyond currently available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and annotations provided;  all at the same time. The dataset is available here: https://vizta-tof.kl.dfki.de/

Preprint:  https://www.researchgate.net/publication/355395814_TICaM_A_Time-of-flight_In-car_Cabin_Monitoring_Dataset

Video: https://www.youtube.com/watch?v=aqYUY2JzqHU

Contact: Jason Rambach

2. PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image
Authors: Yaxu Xie, Fangwen Shu, Jason Raphael Rambach, Alain Pagani, Didier Stricker

Summary: Piece-wise 3D planar reconstruction provides holistic scene understanding of man-made environments, especially for indoor scenarios. Different from other existing approaches, we start from enforcing cross-task consistency for our multi-task convolutional neural network, PlaneRecNet, which integrates a single-stage instance segmentation network for piece-wise planar segmentation and a depth decoder to reconstruct the scene from a single RGB image.

Preprint: https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-filter/publikation/11908

Contact: Alain Pagani

We are happy to announce that our paper “Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching” has been accepted to the CSCS 2021!

The Computer Science in Cars Symposium (CSCS) is ACM’s flagship event in the field of Car IT. The goal is to bring together scientists, engineers, business representatives, and anyone who shares a passion for solving the myriad of complex problems in vehicle technology and their application to automation, driver and vehicle safety, and driving system safety.

In our work, we place stereo matching in a coarse-to-fine estimation framework to improve runtime and memory requirements while maintaining accuracy. This multiscale framework is tested for two state-of-the-art stereo networks and shows significant improvements in runtime, computational complexity, and memory requirements.

Link to preprint: https://arxiv.org/abs/2110.12769

Title: Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching

Authors: Kumail Raza, René Schuster, Didier Stricker

KI zur Erkennung menschlicher Bewegungen und des Umfeldes

Adaptive Methoden die kontinuierlich dazu lernen (Lebenslanges Lernen), bilden eine zentrale Herausforderung zur Entwicklung von robusten, realitätsnahen KI-Anwendungen. Neben der reichen Historie auf dem Gebiet des allgemeinen kontinuierlichen Lernens („Continual Learning“) hat auch das Themenfeld von kontinuierlichem Lernen für Machinelles Sehen unter Realbedingungen jüngst an Interesse gewonnen.

Ziel des Projektes DECODE ist die Erforschung von kontinuierlich adaptierfähigen Modellen zur Rekonstruktion und dem Verständnis von menschlicher Bewegung und des Umfeldes in anwendungsbezogenen Umgebungen. Dazu sollen mobile, visuelle und inertiale Sensoren (Beschleunigungs- und Drehratensensoren) verwendet werden. Für diese verschiedenen Typen an Sensoren und Daten sollen unterschiedliche Ansätze aus dem Bereich des Continual Learnings erforscht und entwickelt werden um einen problemlosen Transfer von Laborbedingungen zu alltäglichen, realistischen Szenarien zu gewährleisten. Dabei konzentrieren sich die Arbeiten auf die Verbesserung in den Bereichen der semantischen Segmentierung von Bildern und Videos, der Schätzung von Kinematik und Pose des menschlichen Körpers sowie der Repräsentation von Bewegungen und deren Kontext. Das Feld potentieller Anwendungsgebiete für die in DECODE entwickelten Methoden ist weitreichend und umfasst eine detaillierte ergonomische Analyse von Mensch-Maschine Interaktionen zum Beispiel am Arbeitsplatz, in Fabriken, oder in Fahrzeugen.

Weitere Informationen: https://www.dfki.de/web/forschung/projekte-publikationen/projekte-uebersicht/projekt/decode

Contact: René Schuster

DFKI Augmented Vision is working with Stellantis on the topic of Radar-Camera Fusion for Automotive Object Detection using Deep Learning since 2020. The collaboration has already led to two publications, in ICCV 2021 (International Conference on Computer Vision – ERCVAD Workshop on “Embedded and Real-World Computer Vision in Autonomous Driving”) and WACV 2022 (Winter Conference on Applications of Computer Vision).

The 2 publications are:

1.  Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization,  Proceedings of the IEEE International Conference on Computer Vision Workshops – ERCVAD Workshop on Embedded and Real-World Computer Vision in Autonomous Driving

Lukas Stefan Stäcker, Juncong Fei, Philipp Heidenreich, Frank Bonarens, Jason Rambach, Didier Stricker, Christoph Stiller

This paper discusses the optimization of neural network based algorithms for object detection based on camera, radar, or lidar data in order to deploy them on an embedded system on a vehicle.

2. Fusion Point Pruning for Optimized 2D Object Detection with Radar-Camera Fusion,  Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2022

Lukas Stefan Stäcker, Juncong Fei, Philipp Heidenreich, Frank Bonarens, Jason Rambach, Didier Stricker, Christoph Stiller

This paper introduces fusion point pruning, a new method to optimize the selection of fusion points within the deep learning network architecture for radar-camera fusion.

Please view the abstract here: Fusion Point Pruning for Optimized 2D Object Detection with Radar-Camera Fusion (dfki.de)

Contact: Dr. Jason Rambach

As part of the research activities of DFKI Augmented Vision in the VIZTA project (https://www.vizta-ecsel.eu/), two publicly available datasets have been released and are available for download. TIMo dataset is a building indoor monitoring dataset for person detection, person counting, and anomaly detection. TICaM dataset is an automotive in-cabin monitoring dataset with a wide field of view for person detection and segmentation and activity recognition. Real and synthetic images are provided allowing for benchmarking of transfer learning algorithms as well. Both datasets are available here https://vizta-tof.kl.dfki.de/. The publication describing the datasets in detail are available as preprints.

TICaM: https://arxiv.org/pdf/2103.11719.pdf

TIMo: https://arxiv.org/pdf/2108.12196.pdf

Video: https://www.youtube.com/watch?v=xWCor9obttA

Contacts: Dr. Jason Rambach, Dr. Bruno Mirbach

On July 29^th, 2021, Dr. Jason Rambach presented the survey paper “A Survey on Applications of Augmented, Mixed and Virtual Reality for Nature and Environment” at the 23^rd Human Computer Interaction Conference HCI International. The article is the result of a collaboration between DFKI, the Worms University of Applied Sciences and the University of Kaiserslautern.

Abstract: Augmented, virtual and mixed reality (AR/VR/MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing. However, the possibilities that AR/VR/MR offer in the area of environmental applications are not yet widely explored. In this paper, we present the outcome of a survey meant to discover and classify existing AR/VR/MR applications that can benefit the environment or increase awareness on environmental issues. We performed an exhaustive search over several online publication access platforms and past proceedings of major conferences in the fields of AR/VR/MR. Identified relevant papers were filtered based on novelty, technical soundness, impact and topic relevance, and classified into different categories. Referring to the selected papers, we discuss how the applications of each category are contributing to environmental protection and awareness. We further analyze these approaches as well as possible future directions in the scope of existing and upcoming AR/VR/MR enabling technologies.

Authors: Jason Rambach, Gergana Lilligreen, Alexander Schäfer, Ramya Bankanal, Alexander Wiebel, Didier Stricker

Paper: https://av.dfki.de/publications/a-survey-on-applications-of-augmented-mixed-and-virtual-reality-for-nature-and-environment/

Contact: Jason.Rambach@dfki.de

DFKI participates in the VIZTA project, coordinated by ST Micrelectronics, aiming  at developing innovative technologies in the field of optical sensors and laser sources for short to long-range 3D-imaging and to demonstrate their value in several key applications including automotive, security, smart buildings, mobile robotics for smart cities, and industry 4.0. The 24-month review by the EU-commission was completed and a public summary of the project was released, including updates from DFKI Augmented Vision on time-of-flight camera dataset recording and deep learning algorithm development for car in-cabin monitoring and smart building person counting and anomaly detection applications.

Please click here to check out the complete summary: https://www.vizta-ecsel.eu/newsletter-april-2021/

Contact: Dr. Jason Rambach, Dr. Bruno Mirbach

We are happy to announce that our paper “SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATIVE EVALUATION OF IMAGE AND POINT CLOUD BASED METHODS” has been accepted for publication at the ICIP 2021 IEEE International Conference on Image Processing which will take place from September 19th to 22nd, 2021 at Anchorage, Alaska, USA.

Abstract: The problem of semantic segmentation from depth images can be addressed by segmenting directly in the image domain or at 3D point cloud level. In this paper, we attempt for the first time to provide a study and experimental comparison of the two approaches. Through experiments on three datasets, namely SUN RGB-D, NYUdV2 and TICaM, we extensively compare various semantic segmentation algorithms, the input to which includes images and point clouds derived from them. Based on this, we offer analysis of the performance and computational cost of these algorithms that can provide guidelines on when each method should be preferred.

Authors: Jigyasa Katrolia, Lars Krämer, Jason Rambach, Bruno Mirbach, Didier Stricker

Paper: https://av.dfki.de/publications/semantic-segmentation-in-depth-data-a-comparative-evaluation-ofimage-and-point-cloud-based-methods/

Contact: Jigyasa_Singh.Katrolia@dfki.de, Jason.Rambach@dfki.de

As part of the research activities of DFKI Augmented Vision in the VIZTA project (https://www.vizta-ecsel.eu/), we have published the open-source dataset for automotive in-cabin monitoring with a wide-angle time-of-flight depth sensor. The TiCAM dataset represents a variety of in-car person behavior scenarios and is annotated with 2D/3D bounding boxes, segmentation masks and person activity labels. The dataset is available here https://vizta-tof.kl.dfki.de/. The publication describing the dataset in detail is available as a preprint here: https://arxiv.org/pdf/2103.11719.pdf

Contacts: Jason Rambach, Jigyasa Katrolia

We are delighted to announce that our paper “PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN” has been accepted for publication at the ICRA 2021 IEEE International Conference on Robotics and Automation which will take place from May 30 to June 5, 2021 at Xi’an, China.

Abstract: Instance segmentation of planar regions in indoor scenes benefits  visual  SLAM  and  other  applications  such  as augmented reality (AR) where scene understanding is required. Existing  methods  built  upon  two-stage  frameworks  show  satisfactory  accuracy  but  are  limited  by  low  frame  rates.  In this  work,  we  propose  a  real-time  deep  neural  architecture that  estimates  piece-wise  planar  regions  from  a  single  RGB image. Our model employs a variant of a fast single-stage CNN architecture to segment plane instances.  Considering  the  particularity of the target detected, we propose Fast Feature Non-maximum  Suppression  (FF-NMS)  to  reduce  the  suppression errors  resulted  from  overlapping  bounding  boxes  of  planes. We  also  utilize  a  Residual  Feature  Augmentation  module  in the  Feature  Pyramid  Network  (FPN)  .  Our  method  achieves significantly  higher  frame-rates  and  comparable  segmentation accuracy  against  two-stage  methods.  We automatically label over 70,000 images as ground truth from the Stanford 2D-3D-Semantics dataset. Moreover, we incorporate our method with a state-of-the-art planar SLAM and validate  its  benefits.

Authors: Yaxu Xie, Jason Raphael Rambach, Fangwen Shu, Didier Stricker

Paper: https://av.dfki.de/publications/planesegnet-fast-and-robust-plane-estimation-using-a-single-stage-instance-segmentation-cnn/

Contact: Yaxu.Xie@dfki.de, Jason.Rambach@dfki.de