2 research outputs found
Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization
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 multi-view correlations via an embedding,
which helps in extracting a diverse set of representatives. The second is to
use a `2;1- norm 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.Comment: IEEE Trans. on Multimedia, 2017 (In Press
Video Skimming: Taxonomy and Comprehensive Survey
Video skimming, also known as dynamic video summarization, generates a
temporally abridged version of a given video. Skimming can be achieved by
identifying significant components either in uni-modal or multi-modal features
extracted from the video. Being dynamic in nature, video skimming, through
temporal connectivity, allows better understanding of the video from its
summary. Having this obvious advantage, recently, video skimming has drawn the
focus of many researchers benefiting from the easy availability of the required
computing resources. In this paper, we provide a comprehensive survey on video
skimming focusing on the substantial amount of literature from the past decade.
We present a taxonomy of video skimming approaches, and discuss their evolution
highlighting key advances. We also provide a study on the components required
for the evaluation of a video skimming performance