6,010 research outputs found
Precise motion descriptors extraction from stereoscopic footage using DaVinci DM6446
A novel approach to extract target motion descriptors in multi-camera video surveillance systems is presented. Using two static surveillance cameras with partially overlapped field of view (FOV), control points (unique points from each camera) are identified in regions of interest (ROI) from both cameras footage. The control points within the ROI are matched for correspondence and a meshed Euclidean distance based signature is computed. A depth map is estimated using disparity of each control pair and the ROI is graded into number of regions with the help of relative depth information of the control points. The graded regions of different depths will help calculate accurately the pace of the moving target and also its 3D location. The advantage of estimating a depth map for background static control points over depth map of the target itself is its accuracy and robustness to outliers. The performance of the algorithm is evaluated in the paper using several test sequences. Implementation issues of the algorithm onto the TI DaVinci DM6446 platform are considered in the paper
Object-based 2D-to-3D video conversion for effective stereoscopic content generation in 3D-TV applications
Three-dimensional television (3D-TV) has gained increasing popularity in the broadcasting domain, as it enables enhanced viewing experiences in comparison to conventional two-dimensional (2D) TV. However, its application has been constrained due to the lack of essential contents, i.e., stereoscopic videos. To alleviate such content shortage, an economical and practical solution is to reuse the huge media resources that are available in monoscopic 2D and convert them to stereoscopic 3D. Although stereoscopic video can be generated from monoscopic sequences using depth measurements extracted from cues like focus blur, motion and size, the quality of the resulting video may be poor as such measurements are usually arbitrarily defined and appear inconsistent with the real scenes. To help solve this problem, a novel method for object-based stereoscopic video generation is proposed which features i) optical-flow based occlusion reasoning in determining depth ordinal, ii) object segmentation using improved region-growing from masks of determined depth layers, and iii) a hybrid depth estimation scheme using content-based matching (inside a small library of true stereo image pairs) and depth-ordinal based regularization. Comprehensive experiments have validated the effectiveness of our proposed 2D-to-3D conversion method in generating stereoscopic videos of consistent depth measurements for 3D-TV applications
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels
In the context of scene understanding, a variety of methods exists to
estimate different information channels from mono or stereo images, including
disparity, depth, and normals. Although several advances have been reported in
the recent years for these tasks, the estimated information is often imprecise
particularly near depth discontinuities or creases. Studies have however shown
that precisely such depth edges carry critical cues for the perception of
shape, and play important roles in tasks like depth-based segmentation or
foreground selection. Unfortunately, the currently extracted channels often
carry conflicting signals, making it difficult for subsequent applications to
effectively use them. In this paper, we focus on the problem of obtaining
high-precision depth edges (i.e., depth contours and creases) by jointly
analyzing such unreliable information channels. We propose DepthCut, a
data-driven fusion of the channels using a convolutional neural network trained
on a large dataset with known depth. The resulting depth edges can be used for
segmentation, decomposing a scene into depth layers with relatively flat depth,
or improving the accuracy of the depth estimate near depth edges by
constraining its gradients to agree with these edges. Quantitatively, we
compare against 15 variants of baselines and demonstrate that our depth edges
result in an improved segmentation performance and an improved depth estimate
near depth edges compared to data-agnostic channel fusion. Qualitatively, we
demonstrate that the depth edges result in superior segmentation and depth
orderings.Comment: 12 page
Online Mutual Foreground Segmentation for Multispectral Stereo Videos
The segmentation of video sequences into foreground and background regions is
a low-level process commonly used in video content analysis and smart
surveillance applications. Using a multispectral camera setup can improve this
process by providing more diverse data to help identify objects despite adverse
imaging conditions. The registration of several data sources is however not
trivial if the appearance of objects produced by each sensor differs
substantially. This problem is further complicated when parallax effects cannot
be ignored when using close-range stereo pairs. In this work, we present a new
method to simultaneously tackle multispectral segmentation and stereo
registration. Using an iterative procedure, we estimate the labeling result for
one problem using the provisional result of the other. Our approach is based on
the alternating minimization of two energy functions that are linked through
the use of dynamic priors. We rely on the integration of shape and appearance
cues to find proper multispectral correspondences, and to properly segment
objects in low contrast regions. We also formulate our model as a frame
processing pipeline using higher order terms to improve the temporal coherence
of our results. Our method is evaluated under different configurations on
multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018
Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics
Video summarization is a timely and rapidly developing research field with broad commercial interest, due to the increasing availability of massive video data. Relevant algorithms face the challenge of needing to achieve a careful balance between summary compactness, enjoyability, and content coverage. The specific case of stereoscopic 3D theatrical films has become more important over the past years, but not received corresponding research attention. In this paper, a multi-stage, multimodal summarization process for such stereoscopic movies is proposed, that is able to extract a short, representative video skim conforming to narrative characteristics from a 3D film. At the initial stage, a novel, low-level video frame description method is introduced (frame moments descriptor) that compactly captures informative image statistics from luminance, color, optical flow, and stereoscopic disparity video data, both in a global and in a local scale. Thus, scene texture, illumination, motion, and geometry properties may succinctly be contained within a single frame feature descriptor, which can subsequently be employed as a building block in any key-frame extraction scheme, e.g., for intra-shot frame clustering. The computed key-frames are then used to construct a movie summary in the form of a video skim, which is post-processed in a manner that also considers the audio modality. The next stage of the proposed summarization pipeline essentially performs shot pruning, controlled by a user-provided shot retention parameter, that removes segments from the skim based on the narrative prominence of movie characters in both the visual and the audio modalities. This novel process (multimodal shot pruning) is algebraically modeled as a multimodal matrix column subset selection problem, which is solved using an evolutionary computing approach. Subsequently, disorienting editing effects induced by summarization are dealt with, through manipulation of the video skim. At the last step, the skim is suitably post-processed in order to reduce stereoscopic video defects that may cause visual fatigue
Depth extraction from monocular video using bidirectional energy minimization and initial depth segmentation
In this paper, we propose to extract depth information from a monocular video sequence. When estimating the depth of the current frame, the bidirectional energy minimization in our scheme considers both the previous frame and next frame, which promises a much more robust depth map and reduces the problems associated with occlusion to a certain extent. After getting an initial depth map from bidirectional energy minimization, we further refine the depth map using segmentation by assuming similar depth values in one segmented region. Different from other segmentation algorithms, we use initial depth information together with the original color image to get more reliable segmented regions. Finally, detecting the sky region using a dark channel prior is employed to correct some possibly wrong depth values for outdoor video. The experimental results are much more accurate compared with the state-of-the-art algorithms
Disparity map generation based on trapezoidal camera architecture for multiview video
Visual content acquisition is a strategic functional block of any visual system. Despite its wide possibilities,
the arrangement of cameras for the acquisition of good quality visual content for use in multi-view video
remains a huge challenge. This paper presents the mathematical description of trapezoidal camera
architecture and relationships which facilitate the determination of camera position for visual content
acquisition in multi-view video, and depth map generation. The strong point of Trapezoidal Camera
Architecture is that it allows for adaptive camera topology by which points within the scene, especially the
occluded ones can be optically and geometrically viewed from several different viewpoints either on the
edge of the trapezoid or inside it. The concept of maximum independent set, trapezoid characteristics, and
the fact that the positions of cameras (with the exception of few) differ in their vertical coordinate
description could very well be used to address the issue of occlusion which continues to be a major
problem in computer vision with regards to the generation of depth map
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