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There are several research works on content-based key-frame extraction from videos, because a collection of still images is easier to deliver and comprehend when compared to a long video stream. Girgensohn et al. [12] found that clustering of similar colors between video scenes is an effective way to filter through a large number of key-frames. SmartSkip [13] is an interface that generates key-frames by analyzing the histogram of images every 10 seconds of the video and looking at rapid overall changes in the color and brightness. Li et al. [14] developed an interface that generates shot boundaries using a detection algorithm that identifies transitions between shots. Nevertheless, the techniques that extract thumbnails from each shot are not always efficient for a quick browse of video content, because there might be too many shots in a video (Figure 1, left).

The transition from the implicit user activity log data to a time series requires some form of user interest modeling. Previous research in user interest modeling has explored several functions that map user behavior to levels of interest. In a content-based approach, Ma et al. [5] made the assumption that video viewers are visually attracted by faces and sudden camera motion, as well as by sudden sounds. Olsen and Moon [23] have proposed that the interest function might be related to user interactions. Most notably, Peng et al. [24] have developed a system that connects the interest function to actual user attention, as measured through eye tracking and face recognition. In summary (Table 1), the main drawback of previous related works has been: 1) the inherent difficulty of modeling either user interest according to video cue time, and/or 2) the lack of any common infrastructure available to all users across Web-based video systems. As a remedy, we propose user interest modeling based on implicit user interaction with the video player buttons, which is common along any of the Web-based video systems (TV, mobile, desktop, tablet).

WebVideo Collection Series 4 62

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According to the proposed implicit user-based key-frame detection scheme (Figure 8), we created graphs that facilitated the visual comparison between the original user interest, the ground truth (interesting video segments), and smooth versions of the user interest time series (Figure 9). We explored alternative smoothing windows for each one of the three types of time series (Replay30, Skip30, Composite). We observed that in all cases the smoothing window should be at least 30 seconds (equal to the fixed skip-step), in order to provide a smooth signal with clear peaks. Moreover, we found that the ideal moving window ranges between 30, 45, and 60 seconds for each one of the three videos respectively (Documentary, Cooking, Lecture). Since we have a controlled experiment (equal number of users, time, total interactions), we suggest that the variability of the smoothing window might depend on the number and duration of the interesting video segments. We do not have conclusive results on this issue, because this was an unexpected finding that should be considered for elaboration in further research.

The original user interest time series (Replay30) is compared to alternative smoothed ones in order to select the most suitable moving window for further analysis. We observed that in all cases the smoothing window should be at least 30 seconds.

Next, we visually compared the smooth versions of the component and composite times series to the ground truth (Figure 10). We observed that in most cases the Replay30 time series closely matched the ground truth. Neither the Skip30, nor the composite time series seem to match the ground truth (Figure 10). Therefore, we computed the local maximums of the Replay30 time series for each one of the three videos.

The comparison between the composite (Skip30-Replay30), the components Skip30, Replay30), and the reference time series reveals that the Replay30 time series is the most suitable for further analysis, because it closely matches the ground truth.

The open-source implementation of SocialSkipa is based on simple, modular, and well-established software components. SocialSkip is a cloud-based application, which uses cloud-based resources (bandwidth, processing, storage), open user-terminal software (any video streaming player), and videos provided by open video databases (e.g., YouTube). The SocialSkip architecture does not require any extra equipment beyond a computer and an internet connection. Previous efforts have introduced several applications in order to evaluate methods for understanding video content. The majority of related studies developed stand alone applications in order to avoid the elaborate installation, processing and streaming problems of broadcast systems. In terms of the user-based data, the most relevant work is the Hot-spots tool, which is part of the YouTube Insight video account. The Hot-spots tool is employing the same set of data as suggested here, but there is no open documentation on the technique employed to map user interactions to a graph. Moreover, Hot-spots has been designed as a tool for video authors, but SocialSkip is proposed as a back-end tool that might improve navigation for all video viewers. Most notably, researchers and practitioners have been cooperating for more than a decade on a large-scale video library and tools for analyzing the content of video. The TRECVID workshop series provides a standard-set of videos, tools, and benchmarks, which facilitate the incremental improvement of sense making for videos [28]. In similar way, we provide open access to both source code and the growing data-set of user interactions, which might facilitate further implementations, as well as alternative user-centric key-frame extraction algorithms.

In addition to doing corporate and non-profit video work, he has written and produced no less than four indie movies as well as a number of shorts and a web video series. Along the way he has picked up a few award nominations for his work and a win at the Intendence Film Festival for his Science fiction feature film 'Orthogenesis'.

Local 58 is a horror web series created by cartoonist Kris Straub. The series is a spin off of Straub's Candle Cove creepypasta.[1][2] Currently hosted on the YouTube channel LOCAL58TV, each video in the series is presented as footage of a fictional public access television channel located in Mason County, West Virginia named Local 58, with the call sign WCLV-TV, created in the late 1930s, which is continuously hijacked over a period of decades with a series of ominous and surreal broadcasts.

The series makes use of video and audio degradation to add to the realism and unsettling nature of each video.[3] The series describes itself as "analog horror",[4][5][6] a term that has since been used as a name for a niche subgenre of similar VHS-themed found footage web series that were either inspired by Local 58 or use a similar style and techniques to the series.[7][8] The series has since gained a cult following.[1]

Straub released Local 58's first episode, Weather Service, in 2015 as an experimental standalone piece for his co-owned YouTube channel Chainsawsuit Original.[19] The piece received positive feedback, leading Straub to do two more on the same channel: Contingency and You Are On the Fastest Route.[19] The series was also hosted on the now-defunct web domain local58.info[1] in 2015[3] and was later uploaded onto its own YouTube channel in 2017.[20]

Straub used iMovie to create the first two episodes of Local 58 and Final Cut Pro X for the other episodes.[21] All of the assets used in the series either come from public domain stock media (for example, the "2000's" Local 58 music, a stock track called "Entering Graciously")[22] or created by Straub himself using Clip Studio,[23] Adobe Photoshop and Adobe Flash.

While the series does not appear to have a continuous plot, nearly every episode seems to include cryptic references related to looking at the Moon or at the night sky, as well as references to the in-universe organization known as the Thought Research Initiative (TRI). Straub has identified themes of the series as "stillness, distrust of safety warnings, misuse of mass perception, parallel science that arises from unexamined bad intent, dogmatic thought."[28]

Since its initial debut, Local 58 has inspired the creation of other series with similar themes, including Channel 7, Analog Archives, Eventide Media Center, and Gemini Home Entertainment.[29] There is some difference of opinion about whether it was truly the first within its genre[30] or an early example of a style that was already developing.[19] However, critics agree the series categorically defined the conventions of the genre that would carry forward.[19][31][32] Some also speculate it has indirectly influenced feature-length films such as Skinamarink (2022) which rely heavily on analog aesthetics.[33][34]

A subreddit dedicated to the series was created on August 8, 2016, which as of December 2022 has 25,400 followers.[36] Straub himself has answered some questions about the series on the subreddit.[37]

The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, The test video collection for TRECVID-AVS2019-TRECVID-AVS2021, which contains 1,082,649 web video clips, with even more diverse content, no predominant characteristics and low self-similarity. 2ff7e9595c