171,427 research outputs found
Estimating Sensor Motion from Wide-Field Optical Flow on a Log-Dipolar Sensor
Log-polar image architectures, motivated by the structure of the human visual field, have long been investigated in computer vision for use in estimating motion parameters from an optical flow vector field. Practical problems with this approach have been: (i) dependence on assumed alignment of the visual and motion axes; (ii) sensitivity to occlusion form moving and stationary objects in the central visual field, where much of the numerical sensitivity is concentrated; and (iii) inaccuracy of the log-polar architecture (which is an approximation to the central 20°) for wide-field biological vision. In the present paper, we show that an algorithm based on generalization of the log-polar architecture; termed the log-dipolar sensor, provides a large improvement in performance relative to the usual log-polar sampling. Specifically, our algorithm: (i) is tolerant of large misalignmnet of the optical and motion axes; (ii) is insensitive to significant occlusion by objects of unknown motion; and (iii) represents a more correct analogy to the wide-field structure of human vision. Using the Helmholtz-Hodge decomposition to estimate the optical flow vector field on a log-dipolar sensor, we demonstrate these advantages, using synthetic optical flow maps as well as natural image sequences
What Will I Do Next? The Intention from Motion Experiment
In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal embedded in an action sequence. Since the same action can be performed
with different intentions, this problem is more challenging but yet affordable
as proved by quantitative cognitive studies which exploit the 3D kinematics
acquired through motion capture systems. In this paper, we bridge cognitive and
computer vision studies, by demonstrating the effectiveness of video-based
approaches for the prediction of human intentions. Precisely, we propose
Intention from Motion, a new paradigm where, without using any contextual
information, we consider instantaneous grasping motor acts involving a bottle
in order to forecast why the bottle itself has been reached (to pass it or to
place in a box, or to pour or to drink the liquid inside). We process only the
grasping onsets casting intention prediction as a classification framework.
Leveraging on our multimodal acquisition (3D motion capture data and 2D optical
videos), we compare the most commonly used 3D descriptors from cognitive
studies with state-of-the-art video-based techniques. Since the two analyses
achieve an equivalent performance, we demonstrate that computer vision tools
are effective in capturing the kinematics and facing the cognitive problem of
human intention prediction.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition
Workshop
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of
consecutive frames into account. While elegant and appealing, the idea of using
more than two frames has not yet produced state-of-the-art results. We present
a simple, yet effective fusion approach for multi-frame optical flow that
benefits from longer-term temporal cues. Our method first warps the optical
flow from previous frames to the current, thereby yielding multiple plausible
estimates. It then fuses the complementary information carried by these
estimates into a new optical flow field. At the time of writing, our method
ranks first among published results in the MPI Sintel and KITTI 2015
benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.Comment: Work accepted at IEEE Winter Conference on Applications of Computer
Vision (WACV 2019
APPLICATION OF LUCAS-KANADE DENSE FLOW FOR TERRAIN MOTION IN LANDSLIDE MONITORING APPLICATION
Landslides are natural hazards that can cause severe damage and loss of life. Optical cameras are a low-cost and high-resolution
alternative among many monitoring systems, as their size and capabilities can vary, allowing for flexible implementation and location.
Computer vision is a branch of artificial intelligence that can analyze and understand optical images, using techniques such as
optical flow, image correlation and machine learning. The application of such techniques can estimate the motion vectors, displacement
fields, providing valuable information for landslide detection, monitoring and prediction. However, computer vision also faces
some challenges such as illumination changes, occlusions, image quality, and computational complexity. In this work, a computer
vision approach based on Lucas-Kanade optical dense flow was applied to estimate the motion vectors between consecutive images
obtained during landslide simulations in a laboratory environment. The approach is applied to two experiments that vary in their
illumination and setup parameters to test its applicability. We also discuss the application of this methodology to images from
Sentinel-2 satellite optical sensors for landslide monitoring in real-world scenarios
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