12,188 research outputs found
DC-image for real time compressed video matching
This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity
Video browsing interfaces and applications: a review
We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other
Activity-driven content adaptation for effective video summarisation
In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided
How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation
Real-time semantic segmentation on high-resolution videos is challenging due
to the strict requirements of speed. Recent approaches have utilized the
inter-frame continuity to reduce redundant computation by warping the feature
maps across adjacent frames, greatly speeding up the inference phase. However,
their accuracy drops significantly owing to the imprecise motion estimation and
error accumulation. In this paper, we propose to introduce a simple and
effective correction stage right after the warping stage to form a framework
named Tamed Warping Network (TWNet), aiming to improve the accuracy and
robustness of warping-based models. The experimental results on the Cityscapes
dataset show that with the correction, the accuracy (mIoU) significantly
increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8
FPS. For non-rigid categories such as "human" and "object", the improvements of
IoU are even higher than 18 percentage points
Compressed Video Action Recognition
Training robust deep video representations has proven to be much more
challenging than learning deep image representations. This is in part due to
the enormous size of raw video streams and the high temporal redundancy; the
true and interesting signal is often drowned in too much irrelevant data.
Motivated by that the superfluous information can be reduced by up to two
orders of magnitude by video compression (using H.264, HEVC, etc.), we propose
to train a deep network directly on the compressed video.
This representation has a higher information density, and we found the
training to be easier. In addition, the signals in a compressed video provide
free, albeit noisy, motion information. We propose novel techniques to use them
effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times
faster than ResNet-152. On the task of action recognition, our approach
outperforms all the other methods on the UCF-101, HMDB-51, and Charades
dataset.Comment: CVPR 2018 (Selected for spotlight presentation
Engineering visualization utilizing advanced animation
Engineering visualization is the use of computer graphics to depict engineering analysis and simulation in visual form from project planning through documentation. Graphics displays let engineers see data represented dynamically which permits the quick evaluation of results. The current state of graphics hardware and software generally allows the creation of two types of 3D graphics. The use of animated video as an engineering visualization tool is presented. The engineering, animation, and videography aspects of animated video production are each discussed. Specific issues include the integration of staffing expertise, hardware, software, and the various production processes. A detailed explanation of the animation process reveals the capabilities of this unique engineering visualization method. Automation of animation and video production processes are covered and future directions are proposed
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