Topics Cooperative radar-video tracking of pedestrians from a moving vehicle Multimodal vision Radar pedestrian detection using deep learning Low level point cloud processing Object detection and tracking Liborg - Lidar based mapping LIDAR based odometry Automotive occupancy mapping High dynamic range video capture Monocular visual odometry Obstacle detection based on 3D imaging
Cooperative radar-video tracking of pedestrians from a moving vehicle Click here for a video by IPI researcher David Van Hamme explaining Cooperative sensor fusion for detection and tracking Click here for an article by IPI researchers David Van Hamme and Jan Aelterman: I Can See Clearly Now! Advanced pedestrian detection using radar-video sensor fusion and automatic tone mapping Autonomous vehicles need to be able to detect other road users and roadside hazards at all times and in all conditions. No single sensor is dependable enough for this task, hence sensor fusion is required.
Topics Networked sensors Sensor networks and methods for wellness monitoring of the elderly Collaborative Tracking in Smart Camera Networks Distributed Camera Networks Multi Camera Networks 3D reconstruction using multiple cameras Real-time video mosaicking Scene and human behavior analysis Foreground background segmentation for dynamic camera viewpoints Foreground/background segmentation Automatic analysis of the worker's behaviour Gesture Recognition Behaviour analysis Immersive communication by means of computer vision (iCocoon) Material Analysis using Image Processing Sensor networks and methods for wellness monitoring of the elderly Addressing the challenges of a rapidly-ageing population has become a priority for many Western countries. Our aim is to relieve the pressure from nursing homes’ limited capacity by pursuing the development of an affordable, round-the-clock monitoring solution that can be used in assisted living facilities. This intelligent solution empowers older people to live (semi-) autonomously for a longer period of time by alerting their caregivers when assistance is required.
Topics Real-time monitoring in Additive Manufacturing
Real-time monitoring in Additive Manufacturing In additive manufacturing, items are 3D printed layer-by-layer using materials like plastics, polymers, and metals. Unfortunately, instabilities in the printing process can produce defects like cracks, warping, and pores/voids within the printed item. Our goal at IPI is to develop computer vision systems that identify the creation of these defects in real time, then provide the 3D printer with sufficient information to intervene in a way that corrects, or avoids, the defect. Since these defects can occur over a very short amount of time (< 1 ms), our monitoring systems need to operate at very high speeds while also providing accurate results.
Melt pool monitoring: GPU-based real-time detection of pore defects using dynamic features and machine learning Using high speed cameras and photodiodes (sampling rates > 20 kHz), we are exploring AI models that can highlight defect creation.
Vision2Reuse project: Smart cameras for the automatic monitoring of the quality of reusable packaging
IPI professor Hiep Luong is a Special Issue Editor. Deadline for manuscript submissions is 15 November 2022.
iMatch (Image-based Material Characterization platform) and AM Platform (Additive Manufacturing platform)
The must-attend networking event if you need, offer, or are interested in R&D on Additive Manufacturing
Advanced sensor systems bring a future with zero road casualties
Results of the UGent, VITO, Suez, and Umicore collaboration within the CHARAMBA project (EIT Raw Materials)
The goal of VISION2REUSE is to demonstrate the potential of smart cameras for the automatic monitoring of the quality of reusable packaging in the food and packaging industry. Based on these camera technologies and state-of-the-art machine learning, it will be measured in an accurate and fast way whether the packaging material in question is still suitable for a new reuse cycle or whether it should go to a dedicated end-of-life stream (e.g. recycling).
REACT-EU EFRO, 1/2022 - 12/2023
IPI contributes a course on Sensor Fusion to AutoSens Academy, the world’s leading community for ADAS and autonomous vehicle technology development
IPI researcher Michiel Vlaminck and partners showcase the results of the icon Analyst-PV project with a webinar and article
Ljubomir Jovanov will present at the AI4 Smart Mobility session during Trefdag Digitaal Vlaanderen on 25 November
Demonstration of a real-time monitoring system for 3D metal printing based on AI and active learning
Watch IPI researcher David Van Hamme talk about Cooperative Sensor Fusion research
Bringing pedestrian detection for autos to the next level: cooperative sensor fusion and automatic tone mapping
Brian Booth joined industry leaders earlier this year to speak on the use of AI in additive manufacturing workflows
Flemish technology research on fine-grained air-quality monitoring available to be embraced by government, citizens and industry
Groundbreaking artificial intelligence research enabling a meaningful impact on people, industry and society. IPI researches real-time and power-efficient AI in the edge for various applications.
National project (Flemish EWI), 7/2019 – 6/2022
Researchers in ACHIEVE are designing highly integrated hardware-software components for the implementation of ultra-efficient embedded vision systems as the basis for innovative distributed vision applications
For IPI, the first goal of this project is to design algorithms for distributed multiple targets tracking through a decentralized approach. The second goal is to improve object detection and tracking using a multi-sensor approach. Thermal cameras have promising potential in surveillance applications, especially when combined with optical cameras. The third goal of the project is to provide solutions for behaviour analysis and action recognition. The research will use high-level analysis to automatically determine which cameras observe the same or similar action, such as pedestrians waiting to cross the street. Deep learning is a promising approach.
H2020-MSCA-ITN, 10/2017 – 9/2021