Spatial and temporal scoring for egocentric video summarization

Guo, Zhao; Gao, Lianli; Zhen, Xiantong; Zou, Fang; Shen, Fumin; Zheng, Kai

doi:10.1016/j.neucom.2016.03.083

Cited by 24 publications

(3 citation statements)

References 53 publications

(99 reference statements)

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“…Indeed, they extracted region cues representing high-level of saliency in egocentric video, and then, they applied a regression method to predict the relative importance of any new region based on these cues. Guo et al [78] proposed a method that focuses on extracting video shots which reflect high stable salience, discrimination and representativeness in order to generate compact storyboard summary [79]. Lu et al, inspired from a work about studying links of news articles over time, defined a random walk-based metric that captures event connectivity beyond simple object co-occurrence, to provide a better sense of story [80].…”

Section: Storytellingmentioning

confidence: 99%

Overview of Lifelogging: Current Challenges and Advances

et al. 2021

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Lifelogging is the process of digital tracking of person's daily experiences in varying amounts of details, for a variety of purposes. In recent years, lifelogging has become an increasingly popular area of research due to the growing demands from many applications, such as video surveillance, entertainment, healthcare systems, and intelligent environments. Furthermore, the advancements in devices technology offer the promise to record and store large volumes of personal data in a very cheap manner, using an inexpensive tool. However, the rapid access to this huge deluge of unlabeled and unstructured data and automatically processing it to recognize everyday experiences, still present major challenges. A large number of research have been conducted in recent years to cover different lifelogging aspects, but there is still a lack of studies that provide a comprehensive survey of the available literature, and most of the existing lifelogging surveys generally focus on only one aspect. This review highlights the advances of stateof-the-art in lifelogging from different angles, including its research history, current applications, activity recognition techniques, moment retrieval, storytelling, privacy and security issues, as well as challenges and future research trends.

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Section: Storytellingmentioning

confidence: 99%

Overview of Lifelogging: Current Challenges and Advances

et al. 2021

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“…Moreover, image signature is applied for foreground object detection and then fused with motion information to summarize egocentric video in [ 40 ]. A modularity cut algorithm is employed in [ 41 ] to track objects and use this information for summary generation.…”

Section: Related Workmentioning

confidence: 99%

Video summarization using line segments, angles and conic parts

2017

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Video summarization is a process to extract objects and their activities from a video and represent them in a condensed form. Existing methods for video summarization fail to detect moving (dynamic) objects in the low color contrast area of a video frame due to the pixel intensities of objects and non-objects are almost similar. However, edges of objects are prominent in the low contrast regions. Moreover, to represent objects, geometric primitives (such as lines, arcs) are distinguishable and high level shape descriptors than edges. In this paper, a novel method is proposed for video summarization using geometric primitives such as conic parts, line segments and angles. Using these features, objects are extracted from each video frame. A cost function is applied to measure the dissimilarity of locations of geometric primitives to detect the movement of objects between consecutive frames. The total distance of object movement is calculated and each video frame is assigned a probability score. Finally, a set of key frames is selected based on the probability scores as per user provided skimming ratio or system default skimming ratio. The proposed approach is evaluated using three benchmark datasets—BL-7F, Office, and Lobby. The experimental results show that our approach outperforms the state-of-the-art method in terms of accuracy.

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“…Many existing video summarisation approaches [1, 4, 8–12] only focus on satisfying the visual interestingness of viewers. Some of these approaches based on one feature, such as visual attention [4, 10] and colour feature [1]. Other approaches [8, 9, 11, 12] fuse various visual features to extract important segments for summaries.…”

Section: Introductionmentioning

confidence: 99%

Video summarisation with visual and semantic cues

Liang

2020

IET image process

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Spatial and temporal scoring for egocentric video summarization

Cited by 24 publications

References 53 publications

Overview of Lifelogging: Current Challenges and Advances

Overview of Lifelogging: Current Challenges and Advances

Video summarization using line segments, angles and conic parts

Video summarisation with visual and semantic cues

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