Topics Cooperative fusion for tracking of pedestrians from a moving vehicle Radar-Video sensor fusion LIDAR-Radar-Video sensor fusion Radar pedestrian detection using deep learning Multimodal vision 2D multi-modal video fusion for wide-angle environment perception Visible-thermal video enhancement for detection of road users Automotive High Dynamic Range (HDR) imaging Classic multi-exposure HDR reconstruction Intelligent HDR tone mapping for traffic applications Learning-based HDR video reconstruction and tone mapping Efficient multi-sensor data annotation tool Point-cloud processing Fast Low-level Point-cloud processing Point-cloud based Object detection and tracking Environment mapping and odometry Liborg - Lidar based mapping LIDAR based odometry Monocular visual odometry Automotive occupancy mapping Obstacle detection based on 3D imaging Real-time sensor data processing for autonomous vehicles using Quasar - demo video
Cooperative fusion for tracking of pedestrians from a moving vehicle Autonomous vehicles need to be able to detect other road users and roadside hazards at all times and in all conditions.
Topics Hyperspectral Image Restoration Hyperspectral Multi-sensor Data Fusion Spatial Information Modelling Image classification in hyperspectral images
Hyperspectral Image Restoration Despite advances in sensor technology, hyperspectral (HS) images are inevitably degraded by noise and blur, which can affect information retrieval and content interpretation. Using denoising and deblurring as a preprocessing tool will improve various post-processing tasks, e.g. classification, target detection, unmixing, etc.
We propose a novel restoration algorithm for HS images. Our method first uses PCA to decorrelate the HS images and separate the information content from the noise. The first k PCA channels contain most information of the HS image, and the remaining B ? k PCA channels (where B is the number of spectral bands of HS image) mainly contain noise. If deblurring is performed on these noisy and high-dimensional B ? k PCs, then it will amplify the noise of the data cube and cause high computational cost in processing the data, which is undesirable.
Listen to them discuss sensor fusion, sensors, weather and environmental conditions, and difficult corner cases
Thermal imaging to the rescue, radar advancing at rapid pace and handling the complexity of automotive sensor fusion
Cooperative Sensor Fusion for Autonomous Driving
Hyperspectral and LiDAR Data Fusion and Classification
Fuelling tomorrow's innovation in the field of naval mine countermeasures, in a responsible way. MCM Lab is a collaborative R&D network to bring together Belgian defense, industry and research organizations.
SafeNav will use innovative sensor setups, including cameras to solve the main challenge = to improve the detection performance in difficult conditions
The SafeNav maritime safety project promises a path towards safer and more secure navigation for the navigator on the bridge today and then moving towards remote-operated and autonomous shipping. One key aspect to boost maritime safety is accurate and efficient detection and tracking of vessels and floating objects as well as marine mammals, in order to avoid navigational hazards such as collisions and subsequent damages to ships, crew members and the marine environment.
In SafeNav, IPI will use innovative sensor setups, including cameras to solve the main challenge = to improve the detection performance in difficult conditions: distant or semi-submerged marine animals or containers, waves crests and sun glitters, poor weather conditions.
EU-HORIZON, 9/2022 - 8/2025
The SeaDetect project, part of Europe's LIFE initiative, aims to halt the biodiversity loss due to collisions between ships and cetaceans by implementing and developing new technologies. To considerably reduce this risk of collision and protect marine life and biodiversity, the SeaDetect project aims to develop two innovative, complementary systems. The first is a detection system to be deployed on ships composed of multiple highly sensitive sensors, of which the data will be fused and processed with artificial intelligence in order to detect cetaceans up to 1km. The second is a network of Passive Acoustic Monitoring (PAM) buoys that will detect and triangulate the position of cetaceans in real-time in order to prevent collision for all vessels in usual maritime roads.
IPI's role in the project is to develop novel detection algorithms based on raw data fusion to improve the detection capabilities of the on-board systems.
EU-LIFE, 9/2022 - 8/2026
IPI professor Hiep Luong is a Special Issue Editor. Deadline for manuscript submissions is 15 November 2022.
Advanced sensor systems bring a future with zero road casualties
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
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
Zhixin Guo (October 15) and Maarten Slembrouck (October 20)
NextPerception aims to develop next generation smart perception sensors and enhance the distributed intelligence paradigm to build versatile, secure, reliable, and proactive human monitoring solutions for the health, wellbeing, and automotive domains
IPI investigates cooperative sensor fusion in support of safety and comfort at road intersections especially for vulnerable road users such as pedestrians and bikers
ECSEL JU, 5/2020 – 4/2023