1,794 research outputs found
Benchmarking and Comparing Popular Visual SLAM Algorithms
This paper contains the performance analysis and benchmarking of two popular
visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the
analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The
dataset selected has a large set of image sequences from a Microsoft Kinect
RGB-D sensor with highly accurate and time-synchronized ground truth poses from
a motion capture system. The test sequences selected depict a variety of
problems and camera motions faced by Simultaneous Localization and Mapping
(SLAM) algorithms for the purpose of testing the robustness of the algorithms
in different situations. The evaluation metrics used for the comparison are
Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The analysis
involves comparing the Root Mean Square Error (RMSE) of the two metrics and the
processing time for each algorithm. This paper serves as an important aid in
the selection of SLAM algorithm for different scenes and camera motions. The
analysis helps to realize the limitations of both SLAM methods. This paper also
points out some underlying flaws in the used evaluation metrics.Comment: 7 pages, 4 figure
Reliable camera motion estimation from compressed MPEG videos using machine learning approach
As an important feature in characterizing video content, camera motion has been widely applied in various multimedia and computer vision applications. A novel method for fast and reliable estimation of camera motion from MPEG videos is proposed, using support vector machine for estimation in a regression model trained on a synthesized sequence. Experiments conducted on real sequences show that the proposed method yields much improved results in estimating camera motions while the difficulty in selecting valid macroblocks and motion vectors is skipped
PACE: Human and Camera Motion Estimation from in-the-wild Videos
We present a method to estimate human motion in a global scene from moving
cameras. This is a highly challenging task due to the coupling of human and
camera motions in the video. To address this problem, we propose a joint
optimization framework that disentangles human and camera motions using both
foreground human motion priors and background scene features. Unlike existing
methods that use SLAM as initialization, we propose to tightly integrate SLAM
and human motion priors in an optimization that is inspired by bundle
adjustment. Specifically, we optimize human and camera motions to match both
the observed human pose and scene features. This design combines the strengths
of SLAM and motion priors, which leads to significant improvements in human and
camera motion estimation. We additionally introduce a motion prior that is
suitable for batch optimization, making our approach significantly more
efficient than existing approaches. Finally, we propose a novel synthetic
dataset that enables evaluating camera motion in addition to human motion from
dynamic videos. Experiments on the synthetic and real-world RICH datasets
demonstrate that our approach substantially outperforms prior art in recovering
both human and camera motions.Comment: 3DV 2024. Project page: https://nvlabs.github.io/PACE
Method and apparatus for video stabilization
A method and an apparatus for video stabilization is disclosed for detecting and eliminating unwanted camera motion from a sequence of video images. Motion vectors are first generated based on sample points using a block matching technique, from which a number of possible camera motions are estimated. Among the estimated camera motions, unwanted or undesirable camera motions are then detected and parameterized. A frame remapping process is then applied to relocate the pixels in the current frame, which acts in opposition to the dislocation of pixels due to unwanted camera motions in order to achieve video stabilization.published_or_final_versio
Image stitching with perspective-preserving warping
Image stitching algorithms often adopt the global transformation, such as
homography, and work well for planar scenes or parallax free camera motions.
However, these conditions are easily violated in practice. With casual camera
motions, variable taken views, large depth change, or complex structures, it is
a challenging task for stitching these images. The global transformation model
often provides dreadful stitching results, such as misalignments or projective
distortions, especially perspective distortion. To this end, we suggest a
perspective-preserving warping for image stitching, which spatially combines
local projective transformations and similarity transformation. By weighted
combination scheme, our approach gradually extrapolates the local projective
transformations of the overlapping regions into the non-overlapping regions,
and thus the final warping can smoothly change from projective to similarity.
The proposed method can provide satisfactory alignment accuracy as well as
reduce the projective distortions and maintain the multi-perspective view.
Experiments on a variety of challenging images confirm the efficiency of the
approach.Comment: ISPRS 2016 - XXIII ISPRS Congress: Prague, Czech Republic, 201
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