Fusion of Foreground Object, Spatial and Frequency Domain Motion Information for Video Summarization

Salehin, Md. Musfequs; Paul, Manoranjan

doi:10.1007/978-3-319-30285-0_26

Cited by 7 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much progress has been made in developing a variety of ways to summarize a singleview video in an unsupervised manner or developing supervised algorithms. Various strategies have been studied, including clustering [1], [9], [18], [57], attention modeling [37], saliency based linear regression model [30], super frame segmentation [20], kernel temporal segmentation [53], crowd-sourcing [26], energy minimization [54], [13], storyline graphs [27], submodular maximization [19], determinantal point process [17], [69], archetypal analysis [60], long shortterm memory [68] and maximal biclique finding [7].…”

Section: Related Workmentioning

confidence: 99%

Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Panda

Roy-Chowdhury

2017

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Most traditional video summarization methods are designed to generate effective summaries for single-view videos, and thus they cannot fully exploit the complicated intra and inter-view correlations in summarizing multi-view videos in a camera network. In this paper, with the aim of summarizing multi-view videos, we introduce a novel unsupervised framework via joint embedding and sparse representative selection. The objective function is two-fold. The first is to capture the multiview correlations via an embedding, which helps in extracting a diverse set of representatives. The second is to use a 2,1norm to model the sparsity while selecting representative shots for the summary. We propose to jointly optimize both of the objectives, such that embedding can not only characterize the correlations, but also indicate the requirements of sparse representative selection. We present an efficient alternating algorithm based on half-quadratic minimization to solve the proposed non-smooth and non-convex objective with convergence analysis.A key advantage of the proposed approach with respect to the state-of-the-art is that it can summarize multi-view videos without assuming any prior correspondences/alignment between them, e.g., uncalibrated camera networks. Rigorous experiments on several multi-view datasets demonstrate that our approach clearly outperforms the state-of-the-art methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Panda

Roy-Chowdhury

2017

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

“…Objects in a video are described by Histograms of Optical Flow Orientations (HOFO) in [ 38 ] and their activities are detected by the Support Vector Machine (SVM) classifier. In [ 39 ], moving object and motion information calculated in spatial and frequency domain are combined for video summarization.…”

Section: Related Workmentioning

confidence: 99%

Video summarization using line segments, angles and conic parts

2017

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Video Summarization Using Geometric Primitives

Salehin¹,

Paul²

2016

2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

View full text Add to dashboard Cite

Fusion of Foreground Object, Spatial and Frequency Domain Motion Information for Video Summarization

Cited by 7 publications

References 27 publications

Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

Video summarization using line segments, angles and conic parts

Video Summarization Using Geometric Primitives

Contact Info

Product

Resources

About