Abstract
The power of video over still images is the ability to represent dynamic activities. But video browsing and retrieval are inconvenient due to inherent spatiotemporal redundancies, where some time intervals may have no activity, or have activities that occur in a small image region. Video synopsis aims to provide a compact video representation, while preserving the essential activities of the original video.
We present dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times. For example, we can create a ”stroboscopic movie”, where multiple dynamic instances of a moving object are played simultaneously. This is an extension of the still stroboscopic picture.
Previous approaches for video abstraction addressed mostly the temporal redundancy by selecting representative key-frames or time intervals. In dynamic video synopsis the activity is shifted into a significantly shorter period, in which the activity is much denser.
Video examples can be found online in http://www.vision.huji.ac.il/synopsis
Introduction
Video synopsis (or abstraction) is a temporally compact representation that aims to enable video browsing and retrieval. We present an approach to video synopsis which optimally reduces the spatiotemporal redundancy in video. As an example, consider the schematic video clip represented as a space-time volume in Fig. 1. The video begins with a person walking on the ground, and after a period of inactivity a bird is flying in the sky. The inactive frames are omitted in most video abstraction methods. Video synopsis is substantially more compact, by playing the person and the bird simultaneously. This makes an optimal use of image regions by shifting events from their original time interval to another time interval when no other activity takes place at this spatial location. Such manipulations relax the chronological consistency of events as was first presented in
The dynamic video synopsis suggested in this paper is different from previous video abstraction approaches (re- viewed in Sec. 1.1) in the following two properties: (i) The video synopsis is itself a video, expressing the dynamics of the scene. (ii) To reduce as much spatiotemporal redundancy as possible, the relative timing between activities may change. The later point allows for the unique contributions in this paper.
In Sec. 2 we describe a low-level method to produce the synopsis video using optimizations on Markov Random Fields .
In Sec. 3 we present an object-based approach in which objects are extracted from the input video. Similar moving object detection was also done in other object-based video summary methods [7, 5, 16]. However, these methods use object detection for identifying significant key frames and do not combine activities from different time intervals. The detection of moving objects, as was used in our experiments is described in Sect. 1.2
One of the options presented in this work is the ability to display multiple dynamic appearances of a single object. This effect is a generalization of the “stroboscopic” pictures used in traditional video synopsis of moving objects [6, 1].
Since this work presents a video-to-video transformation, the reader is encouraged to view the video examples
Related Work on Video Abstraction
There are two main approaches for video synopsis (or video abstraction). In one approach, a set of salient images (key frames) is selected from the original video sequence. The key frames that are selected are the ones that best rep- resent the video [7, 18]. In another approach a collection of short video sequences is selected [15]. The second approach is less compact, but gives a better impression of the scene dynamics. Those approaches (and others) are described in comprehensive surveys on video abstraction [10, 11].
In both approaches above, entire frames are used as the fundamental building blocks. A different methodology uses mosaic images together with some meta-data for video indexing [6, 13, 12]. In this case the static synopsis image includes objects from different times.
Activity Detection
This work assumes that every input pixel has been labeled with its level of “activity”. Evaluation of the activity level is out of the scope of our work, and can be done using one of various methods for detecting irregularities [4, 17], moving object detection, and object tracking
We have selected for our experiments a simple and commonly used activity indicator, where an input pixel I(x, y, t) is labeled as “active” if its color difference from the temporal median at location (x, y) is larger than a given threshold.
Active pixels are defined by the characteristic function
chi;(p)= 1 if p is active
0 otherwise
To clean the activity indicator from noise, a median filter is applied to chi; before continuing with the synopsis process.
While it is possible to use a continuous activity measure, we have concentrated in this paper on the binary case. A continuous activity measure can be used with almost all equations in this paper with only minor changes.
Video Synopsis by Energy Minimization
Let N frames of an input video sequence be represented in a 3D space-time volume I(x, y, t), where (x, y) are thespatial coordinates of this pixel, and 1 le; t le; N is the framenumber.
We would like to generate a synopsis video S(x, y, t) having the following properties:
The video synopsis S should be substantially shorter than the original video I.
Maximum “activity” from the original video should appear in the synopsis video.
The motion of objects in the video synopsis should be similar to their motion in the original video.
T
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Understanding the characterisrics of Internet Short Video Sharing-Youtube as a case study
The recent two years have witnessed an explosion of nAbstract—Established in 2005, YouTube has become the most successful Internet site providing a new generation of short video sharing service. Today, YouTube alone comprises approximately 20% of all HTTP traffic, or nearly 10% of all traffic on the Internet. Understanding the features of YouTube and similar video sharing sites is thus crucial to their sustainable development and to network traffic engineering.
In this paper, using traces crawled in a 3-month period, we present an in-depth and systematic measurement study on the characteristics of YouTube videos. We find that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their active life span, ratings, and comments. The series of datasets also allows us to identify the growth trend of this fast evolving Internet site in various aspects, which has seldom been explored before.
We also look closely at the social networking aspect of YouTube, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploadersrsquo; choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching or peer-to-peer distribution schemes to efficiently deliver videos to end users.
et- worked video sharing as a new killer Internet application. The most successful site, YouTube, now features over 40 million videos and enjoys 20 million visitors each month [1]. The success of similar sites like GoogleVideo, YahooVideo, MySpace, ClipShack, and VSocial, and the recent expensive acquisition of YouTube by Google, further confirm the mass market interest. Their great achievement lies in the combi- nation of the content-rich videos and, equally or even more importantly, the establishment of a social network. These sites have created a video village on the web, where anyone can be a star, from lip-synching teenage girls to skateboarding dogs. With no doubt, they are changing the content distribution landscape and even the popular culture [2].
Established in 2005, YouTube is one of the fastest-growing websites, and has become the 4th most accessed site in the Internet. It has a significant impact on the Internet traffic distribution, and itself is suffering from severe scalability constraints. Understanding the features of YouTube and similar video sharing sites is crucial to network traffic engineering and to sustainable development of this new generation of service.
In this paper, we present an in-depth and systematic mea- surement study on the characteristics of YouTube videos. We have crawled the YouTube site for a 3-month period in early 2007, and have obtained 27 datasets totaling 2,676,388 videos. This constitutes a significant portion of the entire YouTube video repository, and because most of these videos are accessible from the YouTube homepage in less than 10 clicks, they are generally active and thus representative for measuring the repository. Using this collection of datasets, we find that YouTube videos have noticeably different statistics from traditional streaming videos, in aspects from video length and access pattern, to life span. There are also new features that have not been examined by previous measurement studies, for example, the ratings and comments. In addition, the series of datasets also allows us to identify the growth trend of this fast evolving Internet site in various aspects, which has seldom been explored before.
We also look closely at the social networking aspect of YouTube, as this is a key driving force toward the success of YouTube and similar sites. In particular, we find that the links to related videos generated by uploaderrsquo;s choices form a small-world network. This suggests that the videos have strong correlations with each other, and creates opportunities for developing novel caching or peer-to-peer distribution schemes to efficiently deliver videos to end users.
The rest of the paper is organized as follows. Section II presents some background information and other related work. Section III describes our method of gathering information about YouTube videos, which is analyzed generally in Section IV, while the social networking aspects are analyzed separately in Section V. Section VI discusses the implications of the results, and suggests ways that the YouTube service could be improved. Finally, Section VII concludes the paper.
Online videos existed long before YouTube entered the scene. However, uploading videos, managing, sharing and watching them was very cumbersome due to a lack of an easy-to-use integrated platform. More importantly, the videos distributed by traditional media servers and peer-to-peer file downloads like BitTorrent were standalone units of content.
Each single video was not connected to other related video clips, for example other episodes of a show that the user had just watched. Also, there was very little in the way of content reviews or ratings.
The new generation of video sharing sites, YouTube and its competitors, overcame these problems. They allow content suppliers to upload video effortlessly, automatically converting from many different formats, and to tag uploaded videos with keywords. Users can easily share videos by mailing links to them, or embedding them on web pages or in blogs. Users can also rate and comment on videos, bringing new social aspects to the viewing of videos. Consequently, popular videos can rise to the top in a very organic fashion.
The social network existing in YouTube further enables communities and groups. Videos are no longer independent from each other, and neither are users. This has substantially
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