Video summarization via minimum sparse reconstruction

Mei, Shaohui; Guan, Genliang; Wang, Zhiyong; Wan, Shuai; He, Mingyi; Feng, David Dagan

doi:10.1016/j.patcog.2014.08.002

Cited by 167 publications

(115 citation statements)

References 35 publications

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“…Due to the nature of the CSSP, there is no need for a regularizing function R(C), like the one in [12]. The degree of summary compactness and conciseness is directly regulated by a strict, userprovided parameter C, as in most commonly employed clusteringbased summarization methods.…”

Section: Video Summarization Based On the Column Subset Selectionmentioning

confidence: 99%

“…In [11] and [12] the video summarization problem is formulated in terms of sparse dictionary learning, with extracted keyframes enabling optimal reconstruction of the original video from the selected dictionary. Such an approach implies an interesting and formal definition of a video summary, as the set of key-frames that can linearly reconstruct the full-length video in an algebraic sense.…”

Section: Introductionmentioning

confidence: 99%

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Summarization of human activity videos via low-rank approximation

Mademlis

Tefas

Nikolaidis

et al. 2017

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

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Section: Video Summarization Based On the Column Subset Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Summarization of human activity videos via low-rank approximation

Mademlis

Tefas

Nikolaidis

et al. 2017

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

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“…Several efficient video summarization approaches have been proposed for surveillance video stream such as [6]- [9]. For real-time, generally video summarization approaches utilized motion object detection and extraction as essential process to extract motion information from video sequence [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Improving Video Streams Summarization Using Synthetic Noisy Video Data

Al-Musawi¹,

Hasson²

2015

ijacsa

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Abstract-For monitoring public domains, surveillance camera systems are used. Reviewing and processing any subsequences from large amount of raw video streams is time and space consuming. Many efficient approaches of video summarization were proposed to reduce the amount of irrelevant information. Most of these approaches do not take into consideration the illumination or lighting changes that cause noise in video sequences. In this work, video summarization algorithm for video streams has been proposed using Histogram of Oriented Gradient and Correlation coefficients techniques. This algorithm has been applied on the proposed multi-model dataset which is created by combining the original data and the dynamic synthetic data. This dynamic data is proposed using Random Number Generator function. Experiments on this dataset showed the effectiveness of the proposed algorithm compared with traditional dataset.

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“…The combination of global and local features for video frame representation has also been utilized for keyframe extraction [12]. The minimum sparse reconstruction based algorithms select a set of keyframes (namely a dictionary) that can reconstruct all the frames of a video [13], [14], [15], [16]. Most of the existing VS algorithms are performed in an off-line manner, and only a minority of them encounter the on-line situation [16], [17], [18], though the on-line methods are important for many applications.…”

Section: Introductionmentioning

confidence: 99%

“…For example, if VS techniques are utilized to transmit images on a small unmanned aerial vehicle platform, many aspects may restrict the processing, such as processing speed, onboard RAM, power supply, and etc. Therefore, in this paper, a resource restricted on-line VS technique is proposed by extending our previous Minimum Sparse Reconstruction (OnMSR) based VS [16] for the resource restricted video applications. The resource consumption expensive steps in the OnMSR based VS algorithm are improved by considering limited resource.…”

Section: Introductionmentioning

confidence: 99%

Resource restricted on-line Video Summarization with Minimum Sparse Reconstruction

Mei

Wang

et al. 2015

2015 Picture Coding Symposium (PCS)

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Video Summarization (VS) techniques have been widely utilized to produce a concise video content representation, such that the video content can be quickly explored and the complexity of video based analysis and retrieval applications can be highly reduced. However, little attention has been paid for online applications, especially for resource restricted applications, such as onboard VS. In this paper, our previous on-line Minimum Sparse Reconstruction (OnMSR) based VS algorithm is improved for resources restricted applications by confining the size of keyframes for reconstruction. Specially, an on-line reconstruction keyframe set update strategy is designed to meet the requirement of real-time resource restricted situation. Experimental results on various types of videos demonstrate the performance of OnMSR does not vary much by imposing resource constraint in the proposed resource restricted OnMSR (RR-onMSR) algorithm. As a result, the proposed RR-onMSR is very effective for real-time onboard VS applications.

show abstract

Video summarization via minimum sparse reconstruction

Cited by 167 publications

References 35 publications

Summarization of human activity videos via low-rank approximation

Summarization of human activity videos via low-rank approximation

Improving Video Streams Summarization Using Synthetic Noisy Video Data

Resource restricted on-line Video Summarization with Minimum Sparse Reconstruction

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