Multiscale motion saliency for keyframe extraction from motion capture sequences

Halit, Cihan; Çapın, Tolga

doi:10.1002/cav.380

Cited by 37 publications

(39 citation statements)

References 21 publications

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“…Jin et al in [41] compare the proposed method with the UnS and Principal Component Analysis methods [56]. The results show that the proposed method achieves much better reconstruction of skeletal and mesh animation than the other methods under analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Other application of key-frames to content description was proposed by Sano et al [77]. Here, the authors proposed and discussed how the AVDP profile of the MPEG-7 can be applied to multiview 3D video content [56].…”

Section: Content Descriptionmentioning

confidence: 99%

“…Lee et al [25] presented a method to create a single image summary of a 2D or 3D animation, which can be used in the same application as Assa's work. Halit et al [56] proposed a tool for thumbnail generation from motion animation sequences. Several authors, as [82][83][84][85] have used key-frame extraction methods in the 2D-to-3D video conversion.…”

Section: Othersmentioning

confidence: 99%

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Towards key-frame extraction methods for 3D video: a review

Ferreira

Cruz

Assunção

2016

J Image Video Proc.

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The increasing rate of creation and use of 3D video content leads to a pressing need for methods capable of lowering the cost of 3D video searching, browsing and indexing operations, with improved content selection performance. Video summarisation methods specifically tailored for 3D video content fulfil these requirements. This paper presents a review of the state-of-the-art of a crucial component of 3D video summarisation algorithms: the key-frame extraction methods. The methods reviewed cover 3D video key-frame extraction as well as shot boundary detection methods specific for use in 3D video. The performance metrics used to evaluate the key-frame extraction methods and the summaries derived from those key-frames are presented and discussed. The applications of these methods are also presented and discussed, followed by an exposition about current research challenges on 3D video summarisation methods.Keywords: 3D key-frames extraction, 3D video summarisation, Shot boundary detection ReviewIn the last years, new features have been implemented in video applications and terminal equipments due to users demand, who are always seeking for new viewing experiences more interactive and immersive, such as those provided by 3D video. This new visual experience is created by depth information that is part of 3D video and absent in classic 2D video. The inclusion of depth information in video signals is not a recent innovation, but the interest in this type of content and aspects related to it, such as acquisition, analysis, coding, transmission and visualisation, have been increasing recently [1,2]. Lately, 3D video has been attracting attention from industry, namely content producers, equipment providers, distributors and from the research community mostly on account of the improvements in Quality of Experience that it provides to viewers [3], as well as due to the new business opportunities presented by this emerging multimedia format. In the past, video repositories were relatively small so that indexing and retrieval operations were easy to perform. More recently, the massification of 3D video and its applications have resulted in the generation of huge amounts of data, increasing the need for methods that can efficiently index, search, browse and summarise the relevant information with minimum human intervention. Furthermore, 3D video description and management is also required to enable quick presentation of the most important information in a user-friendly manner [4,5]. Video summarisation is a video-content representation method that can fulfil these requirements. In contrast to summarisation of 2D video, which has been the subject of a significant amount of research, 3D video summarisation is still a relatively unexplored research problem which deserves more attention.A video summary is a short version of a full-length video that preserves the essential visual and semantic information of the original unabridged content. In the video summarisation process, a subset of key-frames or a set of shorter video...

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Section: Discussionmentioning

confidence: 99%

Section: Content Descriptionmentioning

confidence: 99%

Section: Othersmentioning

confidence: 99%

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Towards key-frame extraction methods for 3D video: a review

Ferreira

Cruz

Assunção

2016

J Image Video Proc.

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“…The number of keyframes is less than that of the original sequence, but is still greater than one. For example, researchers were able to select around 8% of the frames from motion capture sequences to create key-frame sequences [5].…”

Section: Related Work 21 Pose Selectionmentioning

confidence: 99%

Pose selection for animated scenes and a case study of bas-relief generation

Wang

Guo

Liao

et al. 2017

Proceedings of the Computer Graphics International Conference

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This paper aims to automate the process of generating a meaningful single still image from a temporal input of scene sequences. The success of our extraction relies on evaluating the optimal pose of characters selection, which should maximize the information conveyed. We define the information entropy of the still image candidates as the evaluation criteria.To validate our method and to demonstrate its effectiveness, we generated a relief (as a unique form of art creation) to narrate given temporal action scenes. A user study was conducted to experimentally compare the computer-selected poses with those selected by human participants. The results show that the proposed method can assist the selection of informative pose of character effectively.

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“…The segmentation point reflected an attitude change in the motion data, thus the segmentation point was selected as the keyframe. Halit and Capin [4] also reduced the dimensionality of the data and obtained the motion feature based on the Gaussian-weighted average of the frame, before selecting frames higher than the average as candidate frames and then removing the unimportant frames to yield the final keyframe.…”

Section: Open Accessmentioning

confidence: 99%

Keyframe Extraction from Human Motion Capture Data Based on a Multiple Population Genetic Algorithm

Zhang

Zhou

2014

Symmetry

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Abstract:To reduce reconstruction errors during keyframe extraction and to control the optimal compression ratio, this study proposes a method for keyframe extraction from human motion capture data based on a multiple population genetic algorithm. The fitness function is defined to meet the goals of minimal reconstruction errors and the optimal compression rate, where multiple initial populations are subjected to co-evolution. The multiple population genetic algorithm considers global and local search. Experimental results showed that the algorithm can effectively extract the keyframe from motion capture data and it satisfied the desired reconstruction error.

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Multiscale motion saliency for keyframe extraction from motion capture sequences

Cited by 37 publications

References 21 publications

Towards key-frame extraction methods for 3D video: a review

Towards key-frame extraction methods for 3D video: a review

Pose selection for animated scenes and a case study of bas-relief generation

Keyframe Extraction from Human Motion Capture Data Based on a Multiple Population Genetic Algorithm

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