Towards Distributed Video Summarization

Chakraborty, Sudipta; Tickoo, Omesh; Iyer, Ravishankar K.

doi:10.1145/2733373.2806355

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Video synopsis is a popular approach to solving the video condensation problem, and it provides activity-based video condensation instead of frame-based techniques, such as video fast-forward [ 6 ], video abstraction [ 7 ], video montage [ 8 ], and video summarization [ 9 ]. The video fast-forward method introduced in [ 6 ] can skip some unnecessary frames.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Video Synopsis via Dynamic and Adaptive Online Tube Resizing

Liao

Liu

Cai

2022

Sensors

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Nowadays, with the increased numbers of video cameras, the amount of recorded video is growing. Efficient video browsing and retrieval are critical issues when considering the amount of raw video data to be condensed. Activity-based video synopsis is a popular approach to solving the video condensation problem. However, conventional synopsis methods always consists of complicated and pairwise energy terms that involve a time-consuming optimization problem. In this paper, we propose a simple online video synopsis framework in which the number of collisions of objects is classified first. Different optimization strategies are applied according to different collision situations to maintain a balance among the computational cost, condensation ratio, and collision cost. Secondly, tube-resizing coefficients that are dynamic in different frames are adaptively assigned to a newly generated tube. Therefore, a suitable mapping result can be obtained in order to represent the proper size of the activity in each frame of the synopsis video. The maximum number of activities can be displayed in one frame with minimal collisions. Finally, in order to remove motion anti-facts and improve the visual quality of the condensed video, a smooth term is introduced to constrain the resizing coefficients. Experimental results on extensive videos validate the efficiency of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Real-Time Video Synopsis via Dynamic and Adaptive Online Tube Resizing

Liao

Liu

Cai

2022

Sensors

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“…We apply Delaunay graph clustering to each video separately and then the resulting summaries are combined to form a single summary. (3) a submodularity based method (SubMod) [6], [43] that uses three selection criteria (Exhaustive, Mutually Exclusive and Interestingness) to extract informative segments from a video. We follow [6] to model the first two selection criteria and follow [25] to model interestingness in summarization.…”

Section: A Topic-oriented Video Summarizationmentioning

confidence: 99%

“…(3) a submodularity based method (SubMod) [6], [43] that uses three selection criteria (Exhaustive, Mutually Exclusive and Interestingness) to extract informative segments from a video. We follow [6] to model the first two selection criteria and follow [25] to model interestingness in summarization. We use the same method [26] to compute the interestingness score of each video segment and use a greedy algorithm proposed by Nemhauser et.al.…”

Section: A Topic-oriented Video Summarizationmentioning

confidence: 99%

Diversity-Aware Multi-Video Summarization

Panda

Mithun

Roy-Chowdhury

2017

IEEE Trans. on Image Process.

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Most video summarization approaches have focused on extracting a summary from a single video; we propose an unsupervised framework for summarizing a collection of videos. We observe that each video in the collection may contain some information that other videos do not have, and thus exploring the underlying complementarity could be beneficial in creating a diverse informative summary. We develop a novel diversity-aware sparse optimization method for multi-video summarization by exploring the complementarity within the videos. Our approach extracts a multi-video summary, which is both interesting and representative in describing the whole video collection. To efficiently solve our optimization problem, we develop an alternating minimization algorithm that minimizes the overall objective function with respect to one video at a time while fixing the other videos. Moreover, we introduce a new benchmark data set, Tour20, that contains 140 videos with multiple manually created summaries, which were acquired in a controlled experiment. Finally, by extensive experiments on the new Tour20 data set and several other multi-view data sets, we show that the proposed approach clearly outperforms the state-of-the-art methods on the two problems-topic-oriented video summarization and multi-view video summarization in a camera network.

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Sparse modeling for topic-oriented video summarization

Panda

Roy-Chowdhury

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Towards Distributed Video Summarization

Cited by 5 publications

References 10 publications

Real-Time Video Synopsis via Dynamic and Adaptive Online Tube Resizing

Real-Time Video Synopsis via Dynamic and Adaptive Online Tube Resizing

Diversity-Aware Multi-Video Summarization

Sparse modeling for topic-oriented video summarization

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