TY - CHAP AU - Liu, Chang AU - Szirányi, Tamás TI - Active Wildfires Detection and Dynamic Escape Routes Planning for Humans through Information Fusion between Drones and Satellites T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023 PB - IEEE CY - Piscataway (NJ) PY - 2023 SP - 1977 EP - 1982 PG - 6 DO - 10.1109/ITSC57777.2023.10421956 UR - https://m2.mtmt.hu/api/publication/34504694 ID - 34504694 LA - English DB - MTMT ER - TY - CONF AU - Bugár-Mészáros, Barnabás AU - Majdik, András AU - Rózsa, Zoltán AU - Szirányi, Tamás TI - Radiation Plan Optimization for UV-C Disinfection Robots T2 - Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája PY - 2023 SP - 1 EP - 4 PG - 4 UR - https://m2.mtmt.hu/api/publication/34504666 ID - 34504666 LA - English DB - MTMT ER - TY - CONF AU - Rózsa, Zoltán AU - Szirányi, Tamás TI - LIDAR mérések időbeli felskálázása mono kamera alapján T2 - Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája PY - 2023 SP - 1 EP - 13 PG - 13 UR - https://m2.mtmt.hu/api/publication/34504606 ID - 34504606 LA - Hungarian DB - MTMT ER - TY - CONF AU - Golarits, Marcell AU - Tizedes, László AU - Majdik, András AU - Szirányi, Tamás TI - Towards modelling an atmospheric pseudo-satellite imaging system T2 - Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája PY - 2023 SP - 1 EP - 5 PG - 5 UR - https://m2.mtmt.hu/api/publication/34504590 ID - 34504590 LA - English DB - MTMT ER - TY - CHAP AU - Liu, Chang AU - Szirányi, Tamás TI - Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke T2 - 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) PB - IEEE CY - Piscataway (NJ) SN - 9798350370911 PY - 2023 SP - 429 EP - 434 PG - 6 DO - 10.1109/SITIS61268.2023.00077 UR - https://m2.mtmt.hu/api/publication/34504535 ID - 34504535 AB - In recent years, the increasing prevalence and intensity of wildfires have posed significant challenges to emergency response teams. The utilization of unmanned aerial vehicles (UAVs), commonly known as drones, has shown promise in aiding wildfire management efforts. This work focuses on the development of an optimal wildfire escape route planning system specifically designed for drones, considering dynamic fire and smoke models. First, the location of the source of the wildfire can be well located by information fusion between UAV and satellite, and the road conditions in the vicinity of the fire can be assessed and analyzed using multichannel remote sensing data. Second, the road network can be extracted and segmented in real time using UAV vision technology, and each road in the road network map can be given priority based on the results of road condition classification. Third, the spread model of dynamic fires calculates the new location of the fire source based on the fire intensity, wind speed and direction, and the radius increases as the wildfire spreads. Smoke is generated around the fire source to create a visual representation of a burning fire. Finally, based on the improved A∗ algorithm, which considers all the above factors, the UAV can quickly plan an escape route based on the starting and destination locations that avoid the location of the fire source and the area where it is spreading. By considering dynamic fire and smoke models, the proposed system enhances the safety and efficiency of drone operations in wildfire environments. © 2023 IEEE. LA - English DB - MTMT ER - TY - CHAP AU - Markó, Norbert AU - Szirányi, Tamás AU - Ballagi, Áron TI - Terrain Depth Estimation for Improved Inertial Data Prediction in Autonomous Navigation Systems T2 - 2023 IEEE International Automated Vehicle Validation Conference (IAVVC) PB - IEEE CY - Piscataway (NJ) SN - 9798350322538 PY - 2023 SP - 1 EP - 6 PG - 6 DO - 10.1109/IAVVC57316.2023.10328139 UR - https://m2.mtmt.hu/api/publication/34473012 ID - 34473012 LA - English DB - MTMT ER - TY - JOUR AU - Majdik, András AU - Székely, Z AU - Keszler, Anita AU - Márkus, Zsolt László AU - Szirányi, Tamás TI - Teaching to Survive: A Citizen-centred Disaster Preparedness Project JF - ERCIM NEWS J2 - ERCIM NEWS PY - 2023 IS - 135 SP - 16 EP - 17 PG - 2 SN - 0926-4981 UR - https://m2.mtmt.hu/api/publication/34223038 ID - 34223038 LA - English DB - MTMT ER - TY - CHAP AU - Gazdag, Sándor AU - Pásztornicky, Dániel AU - Jankó, Zsolt AU - Szirányi, Tamás AU - Majdik, András TI - Collaborative Visual-Inertial Localization of Teams With Floorplan Extraction T2 - 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) PB - IEEE CY - Piscataway (NJ) SN - 9798350302615 PY - 2023 SP - 1 EP - 5 PG - 5 DO - 10.1109/ICASSPW59220.2023.10192967 UR - https://m2.mtmt.hu/api/publication/34107692 ID - 34107692 N1 - Institute for Computer Science and Control (SZTAKI), Machine Perception Research Laboratory, Kende u. 13.-17, Budapest, H-1111, Hungary Budapest University of Technology and Economics, Department of Material Handling and Logistics Systems, Bertalan Lajos u. 7, Budapest, H-1111, Hungary Export Date: 1 September 2023 Correspondence Address: Gazdag, S.; Institute for Computer Science and Control (SZTAKI), Kende u. 13.-17, Hungary Correspondence Address: Pasztornicky, D.; Institute for Computer Science and Control (SZTAKI), Kende u. 13.-17, Hungary Correspondence Address: Janko, Z.; Institute for Computer Science and Control (SZTAKI), Kende u. 13.-17, Hungary Correspondence Address: Sziranyi, T.; Institute for Computer Science and Control (SZTAKI), Kende u. 13.-17, Hungary Correspondence Address: Majdik, A.L.; Institute for Computer Science and Control (SZTAKI), Kende u. 13.-17, Hungary LA - English DB - MTMT ER - TY - JOUR AU - Rózsa, Zoltán AU - Szirányi, Tamás TI - Optical Flow and Expansion Based Deep Temporal Up-Sampling of LIDAR Point Clouds JF - REMOTE SENSING J2 - REMOTE SENS-BASEL VL - 15 PY - 2023 IS - 10 PG - 19 SN - 2072-4292 DO - 10.3390/rs15102487 UR - https://m2.mtmt.hu/api/publication/33822096 ID - 33822096 N1 - Export Date: 15 June 2023 Correspondence Address: Rozsa, Z.; Department of Material Handling and Logistics Systems, Muegyetem rkp. 3, Hungary; email: zoltan.rozsa@logisztika.bme.hu AB - This paper proposes a framework that enables the online generation of virtual point clouds relying only on previous camera and point clouds and current camera measurements. The continuous usage of the pipeline generating virtual LIDAR measurements makes the temporal up-sampling of point clouds possible. The only requirement of the system is a camera with a higher frame rate than the LIDAR equipped to the same vehicle, which is usually provided. The pipeline first utilizes optical flow estimations from the available camera frames. Next, optical expansion is used to upgrade it to 3D scene flow. Following that, ground plane fitting is made on the previous LIDAR point cloud. Finally, the estimated scene flow is applied to the previously measured object points to generate the new point cloud. The framework’s efficiency is proved as state-of-the-art performance is achieved on the KITTI dataset. LA - English DB - MTMT ER - TY - CHAP AU - Zhu, Morui AU - Liu, Chang AU - Szirányi, Tamás ED - Imai, F ED - Distante, C ED - Battiato, S TI - A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal T2 - IMPROVE 2023 : Proceedings of the 3rd International Conference on Image Processing and Vision Engineering PB - SciTePress CY - Setubal SN - 9789897586422 PY - 2023 SP - 206 EP - 212 PG - 7 DO - 10.5220/0012039600003497 UR - https://m2.mtmt.hu/api/publication/33775933 ID - 33775933 AB - Due to the inevitable contamination of thick clouds and their shadows, satellite images are greatly affected, which significantly reduces the usability of data from satellite images. Therefore, obtaining high-quality image data without cloud contamination in a specific area and at the time we need it is an important issue. To address this problem, we collected a new multi-temporal dataset covering the entire globe, which is used to remove clouds and their shadows. Since generative adversarial networks (GANs) perform well in conditional image synthesis challenges, we utilized a spatial-temporal GAN (STGAN) to eliminate clouds and their shadows in optical satellite images. As a baseline model, STGAN demonstrated outstanding performance in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), achieving scores of 33.4 and 0.929, respectively. The cloud-free images generated in this work have significant utility for various downstream applications in real-world environments. Dataset is publicly available: https://github.com/zhumorui/SMT-CR LA - English DB - MTMT ER -