Detection of moving objects through turbulent media. Decomposition of Oscillatory vs Non-Oscillatory spatio-temporal vector fields
In this paper, we investigate how moving objects can be detected when images are impacted by atmospheric turbulence. We present a geometric spatio-temporal point of view to the problem and show that it is possible to distinguish movement due to the turbulence vs. moving objects. To perform this task, we propose an extension of 2D cartoon+texture decomposition algorithms to 3D vector fields. Our algorithm is based on curvelet spaces which permit to better characterize the movement flow geometry. We present experiments on real data which illustrate the efficiency of the proposed method.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence
Moving object segmentation in the presence of atmospheric turbulence is highly challenging due to turbulence-induced irregular and time-varying distortions. In this paper, we present an unsupervised approach for segmenti…
ObjectOptical Flow EstimationSegmentationSemantic SegmentationRobust Moving Objects Detection in Lidar Data Exploiting Visual Cues
Detecting moving objects in dynamic scenes from sequences of lidar scans is an important task in object tracking, mapping, localization, and navigation. Many works focus on changes detection in previously observed scenes…
Object TrackingNeuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media
Tracking and acquiring simultaneous optical images of randomly moving targets obscured by scattering media remains a challenging problem of importance to many applications that require precise object localization and ide…
Computational EfficiencyImage ReconstructionObject LocalizationObject TrackingSeeing Through Clutter: Structured 3D Scene Reconstruction via Iterative Object Removal
We present SeeingThroughClutter, a method for reconstructing structured 3D representations from single images by segmenting and modeling objects individually. Prior approaches rely on intermediate tasks such as semantic …
Semantic SegmentationDepth EstimationSpatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation
Moving objects have special importance for Autonomous Driving tasks. Detecting moving objects can be posed as Moving Object Segmentation, by segmenting the object pixels, or Moving Object Detection, by generating a bound…
Autonomous DrivingDecoderMoving Object DetectionMulti-Task Learning+5