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Semantic Depth Map Fusion for Moving Vehicle Detection in Aerial Video
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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We propose an automatic moving vehicle detection system for wide area aerial video based on semantic fusion of motion information with projected building footprint information to significantly reduce the ...
Wide area motion imagery from an aerial platform offers a compelling advantage in providing a global picture of traffic flows for transportation and urban planning that is complementary to the information ...
Aerial WAMI of Albuquerque (ABQ) was collected by Transparent Sky, LLC in Edgewood, NM and provided by Steve Suddarth. ...
doi:10.1109/cvprw.2016.196
dblp:conf/cvpr/PoostchiAVBPS16
fatcat:ze66xd5yqbe6nbcqyqjt7suwlm
Geo-spatial aerial video processing for scene understanding and object tracking
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
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coordinate system for pixels in the video. ...
labeling of structures such as buildings, foliage, and roads for scene understanding, and (2) elimination of moving object detection and tracking errors using 3D parallax constraints and semantic labels ...
After preforming the geo-spatial aerial video process, we generate a series of results including georegistration, camera pose, depth map, road map, scene segmentation and moving object tracking for each ...
doi:10.1109/cvpr.2008.4587434
dblp:conf/cvpr/XiaoCHS08
fatcat:5ejclvnxbzhypdro776px3ovma
2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29
2019
IEEE transactions on circuits and systems for video technology (Print)
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., +, TCSVT May 2019 1408-1422 Comprehensive Analysis of Deep Learning-Based Vehicle Detection in Aerial Images. ...
., +, TCSVT Dec. 2019 3487-3500
Perceiving Motion From Dynamic Memory for Vehicle Detection in Surveil-
lance Videos. ...
doi:10.1109/tcsvt.2019.2959179
fatcat:2bdmsygnonfjnmnvmb72c63tja
Learn on the Fly
[chapter]
2020
Advances in Intelligent Systems and Computing
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It is anticipated that LOTF would have an impact on Artificial Intelligence, video analytics for searching and tracking survivors' activities for humanitarian assistance and disaster relief (HADR), field ...
However, it aims to solve more comprehensive, ill-structured problems such as human activity recognition from a drone video in a disastrous environment. ...
Fortner for his organization. This study is in part sponsored by NIST PSCR /PSIA program and Northrop Grumman Corporation. ...
doi:10.1007/978-3-030-51758-8_8
fatcat:up7c46663vff3ovgr3wc5vefxy
DLRAD – A FIRST LOOK ON THE NEW VISION AND MAPPING BENCHMARK DATASET FOR AUTONOMOUS DRIVING
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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</strong> DLRAD &ndash; a new vision and mapping benchmark dataset for autonomous driving is under development for the validation of intelligent driving algorithms. ...
Stationary, mobile, and airborne sensors monitored simultaneously the environment around a reference vehicle, which was driving on urban, suburb and rural roads in and around the city of Braunschweig/Germany ...
The setup can be used to calculate a depth map or to use the single frame images for classification purposes. ...
doi:10.5194/isprs-archives-xlii-1-251-2018
fatcat:6bfiizukovdftdp5hgfdmu3voa
Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, Challenges
2022
IEEE Access
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The authors in [6] have tackled the problem of unreliable and noisy 3D maps generated by LIDAR sensors for precise mapping and localization of Autonomous vehicles due to the existence of moving objects ...
The authors in [12] address the lack of research in the real-time RGB-D fusion semantic segmentation domain, despite accessible depth information. ...
doi:10.1109/access.2022.3144407
fatcat:27zpuomnxzbs3gl3ab55a46wru
Front Matter: Volume 10806
2018
Tenth International Conference on Digital Image Processing (ICDIP 2018)
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Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. ...
for improving performance of fire control radar based on accumulation detection 10806 11 Moving object detection in videos from hand-held camera 10806 12 Forward vehicle detection method based on geometric ...
aerial videos 10806 0Y Improving methods for detecting people in video recordings using shifting time-windows 10806 0Z Real-time dangerous objects detection in millimeter wave images 10806 10 Method ...
doi:10.1117/12.2510343
fatcat:maohjht2t5apneao4iivotwxey
Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network
2018
IEEE Transactions on Geoscience and Remote Sensing
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In contrast, vehicle detection and semantic segmentation each only concern one of the two. We propose to tackle this problem with a semantic boundary-aware multi-task learning network. ...
Currently, datasets with pixel-wise annotation for vehicle extraction are ISPRS dataset and IEEE GRSS DFC2015 dataset over Zeebrugge, which specializes in semantic segmentation. ...
ACKNOWLEDGMENT The authors would like to thank the ISPRS for making the Potsdam data set available. ...
doi:10.1109/tgrs.2018.2841808
fatcat:qrk42qlhsrglriju33enxtefum
Large-Scale Synthetic Urban Dataset for Aerial Scene Understanding
2020
IEEE Access
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In this way, synthetic aligned multi-model data are obtained efficiently, including spectral images, semantic labels, depth and normal maps. ...
throughout the scene, virtual cameras moved according to realistic trajectories of unmanned aerial vehicles (UAVs). ...
For this problem, depth maps, semantic labels, and light maps can all be used for 2D/3D scene understanding. ...
doi:10.1109/access.2020.2976686
fatcat:fwqoi6qjore3jku6jwmdrcayp4
Vehicle Detection in Aerial Images Based on 3D Depth Maps and Deep Neural Networks
2021
IEEE Access
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INDEX TERMS Convolutional neural networks, 3D depth maps, object detection, aerial images. ...
In this article, we investigate the ability of three-dimensional (3D) feature maps to improve the performance of deep neural network (DNN) for vehicle detection. ...
their support in this work. ...
doi:10.1109/access.2021.3049741
fatcat:wy7sqskjpbarza6ty2ysahvipe
Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery
[article]
2017
arXiv
pre-print
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The experiments on extensive VOTC2016 benchmark dataset and aerial video confirm that combining complementary tracking cues in an intelligent fusion framework enables persistent tracking for Full Motion ...
We proposed a multi-component framework based on semantic fusion of motion information with projected building footprint map to significantly reduce the false alarm rate in urban scenes with many tall ...
Context-aware Moving Vehicle Detection Using 2D Depth Maps We studied that using purely conventional moving object detection methods would not be sufficient for a wide aerial motion imagery in which there ...
arXiv:1711.01656v1
fatcat:w5okzgmz5ng5thxwq2p7dvwl6a
Siamese Tracking from Single Point Initialization
2019
Sensors
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Recently, we have been concerned with locating and tracking vehicles in aerial videos. Vehicles in aerial videos usually have small sizes due to use of cameras from a remote distance. ...
For the purpose of target locating and tracking in our system, detecting the contour of the target is utilized and can help with improving the accuracy of target tracking, because a shape-adaptive template ...
Results on DARPA VIVID Datasets
Qualitative Evaluation As mentioned before, in our program, the images were taken from distance by a camera on a fast-moving unmanned aerial vehicle. ...
doi:10.3390/s19030514
fatcat:5vypa3tsybghfmsasrxok3qwiq
SDWBF Algorithm: A Novel Pedestrian Detection Algorithm in the Aerial Scene
2022
Drones
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Due to the large amount of video data from UAV aerial photography and the small target size from the aerial perspective, pedestrian detection in drone videos remains a challenge. ...
To detect objects in UAV images quickly and accurately, a small-sized pedestrian detection algorithm based on the weighted fusion of static and dynamic bounding boxes is proposed. ...
Intelligent transportation uses vehicle detection and pedestrian detection technology to detect vehicle flow and pedestrian flow information in real time [5] . ...
doi:10.3390/drones6030076
fatcat:nlp4qsrjnbgd5i3x2v27rp7yca
A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles
2020
Journal of Imaging
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The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. ...
This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation ...
Vehicle Detection Vehicle detection in aerial images is a crucial image processing step for many applications like screening of large areas. ...
doi:10.3390/jimaging6080078
pmid:34460693
pmcid:PMC8321148
fatcat:ds4kpheadvg6xp2fambrp6nffq
Expanding Navigation Systems by Integrating It with Advanced Technologies
[chapter]
2020
Geographic Information Systems in Geospatial Intelligence [Working Title]
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In the next section, we outline the limitations of GPS, and then we describe the integration of GIS, smartphones, and GPS to enable its use in mobile applications. ...
For the rest of this chapter, we introduce various navigation implementations using alternate technologies integrated with GPS or operated as standalone devices. ...
Micro aerial vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment. ...
doi:10.5772/intechopen.91203
fatcat:gbb2jeylmffc3gqlmbahfy54ee
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