11,813 research outputs found
Automatic Feature-Based Stabilization of Video with Intentional Motion through a Particle Filter
Video sequences acquired by a camera mounted on a hand held device or a mobile platform are affected by unwanted shakes and jitters. In this situation, the performance of video applications, such us motion segmentation and tracking, might dramatically be decreased. Several digital video stabilization approaches have been proposed to overcome this problem. However, they are mainly based on motion estimation techniques that are prone to errors, and thus affecting the stabilization performance. On the other hand, these techniques can only obtain a successfully stabilization if the intentional camera motion is smooth, since they incorrectly filter abrupt changes in the intentional motion. In this paper a novel video stabilization technique that overcomes the aforementioned problems is presented. The motion is estimated by means of a sophisticated feature-based technique that is robust to errors, which could bias the estimation. The unwanted camera motion is filtered, while the intentional motion is successfully preserved thanks to a Particle Filter framework that is able to deal with abrupt changes in the intentional motion. The obtained results confirm the effectiveness of the proposed algorith
Performance improvement in VSLAM using stabilized feature points
Simultaneous localization and mapping (SLAM) is the main prerequisite for the autonomy of a mobile robot. In this paper, we present a novel method that enhances the consistency of the map using stabilized corner features. The proposed method integrates template matching based video stabilization and Harris corner detector. Extracting Harris corner features from stabilized video consistently increases the accuracy of the localization. Data coming from a video camera and odometry are fused in an Extended Kalman Filter (EKF) to determine the pose of the robot and build the map of the environment. Simulation results validate the performance improvement obtained by the proposed technique
Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
In recent years, heatmap regression based models have shown their
effectiveness in face alignment and pose estimation. However, Conventional
Heatmap Regression (CHR) is not accurate nor stable when dealing with
high-resolution facial videos, since it finds the maximum activated location in
heatmaps which are generated from rounding coordinates, and thus leads to
quantization errors when scaling back to the original high-resolution space. In
this paper, we propose a Fractional Heatmap Regression (FHR) for
high-resolution video-based face alignment. The proposed FHR can accurately
estimate the fractional part according to the 2D Gaussian function by sampling
three points in heatmaps. To further stabilize the landmarks among continuous
video frames while maintaining the precise at the same time, we propose a novel
stabilization loss that contains two terms to address time delay and non-smooth
issues, respectively. Experiments on 300W, 300-VW and Talking Face datasets
clearly demonstrate that the proposed method is more accurate and stable than
the state-of-the-art models.Comment: Accepted to AAAI 2019. 8 pages, 7 figure
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Electronic Image Stabilization for Mobile Robotic Vision Systems
When a camera is affixed on a dynamic mobile robot, image stabilization is the first step towards more complex analysis on the video feed. This thesis presents a novel electronic image stabilization (EIS) algorithm for small inexpensive highly dynamic mobile robotic platforms with onboard camera systems. The algorithm combines optical flow motion parameter estimation with angular rate data provided by a strapdown inertial measurement unit (IMU). A discrete Kalman filter in feedforward configuration is used for optimal fusion of the two data sources. Performance evaluations are conducted by a simulated video truth model (capturing the effects of image translation, rotation, blurring, and moving objects), and live test data. Live data was collected from a camera and IMU affixed to the DAGSI Whegs™ mobile robotic platform as it navigated through a hallway. Template matching, feature detection, optical flow, and inertial measurement techniques are compared and analyzed to determine the most suitable algorithm for this specific type of image stabilization. Pyramidal Lucas-Kanade optical flow using Shi-Tomasi good features in combination with inertial measurement is the EIS algorithm found to be superior. In the presence of moving objects, fusion of inertial measurement reduces optical flow root-mean-squared (RMS) error in motion parameter estimates by 40%. No previous image stabilization algorithm to date directly fuses optical flow estimation with inertial measurement by way of Kalman filtering
Inertial-Magnetic Sensors for Assessing Spatial Cognition in Infants
This paper describes a novel approach to the
assessment of spatial cognition in children. In particular we
present a wireless instrumented toy embedding magneto-inertial
sensors for orientation tracking, specifically developed to assess
the ability to insert objects into holes. To be used in naturalistic
environments (e.g. daycares), we also describe an in-field calibration
procedure based on a sequence of manual rotations, not
relying on accurate motions or sophisticated equipment.
The final accuracy of the proposed system, after the mentioned
calibration procedure, is derived by direct comparison with
a gold-standard motion tracking device. In particular, both
systems are subjected to a sequence of ten single-axis rotations
(approximately 90 deg, back and forth), about three different
axes. The root-mean-square of the angular error between the
two measurements (gold-standard vs. proposed systems) was
evaluated for each trial. In particular, the average rms error
is under 2 deg.
This study indicates that a technological approach to ecological
assessment of spatial cognition in infants is indeed feasible. As
a consequence, prevention through screening of large number of
infants is at reach
Deep Burst Denoising
Noise is an inherent issue of low-light image capture, one which is
exacerbated on mobile devices due to their narrow apertures and small sensors.
One strategy for mitigating noise in a low-light situation is to increase the
shutter time of the camera, thus allowing each photosite to integrate more
light and decrease noise variance. However, there are two downsides of long
exposures: (a) bright regions can exceed the sensor range, and (b) camera and
scene motion will result in blurred images. Another way of gathering more light
is to capture multiple short (thus noisy) frames in a "burst" and intelligently
integrate the content, thus avoiding the above downsides. In this paper, we use
the burst-capture strategy and implement the intelligent integration via a
recurrent fully convolutional deep neural net (CNN). We build our novel,
multiframe architecture to be a simple addition to any single frame denoising
model, and design to handle an arbitrary number of noisy input frames. We show
that it achieves state of the art denoising results on our burst dataset,
improving on the best published multi-frame techniques, such as VBM4D and
FlexISP. Finally, we explore other applications of image enhancement by
integrating content from multiple frames and demonstrate that our DNN
architecture generalizes well to image super-resolution
- …