Multicamera video summarization and anomaly detection from activity motifs

Leo, Carter De; Manjunath, B. S.

doi:10.1145/2530285

Cited by 20 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this method relies on inter-camera frame correspondence, which can be a very difficult problem in uncontrolled settings. The work in [31] and [32] also addresses a similar problem of summarization in non-overlapping camera networks. Learning from multiple information sources such as video tags [64], topic-related web videos [47], [48] and non-visual data [71], [65] is also a recent trend in multiple web video summarization.…”

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

“…An online method for summarization can also be found in [53]. In [38], [39], summarization is performed by detecting abnormal events between sensors in a nonoverlapping camera network.…”

Section: Related Workmentioning

confidence: 99%

Diversity-Aware Multi-Video Summarization

Panda

Mithun

Roy-Chowdhury

2017

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

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.

show abstract

“…Fu et al [26] addressed generating concise multi-view video summaries by multi-objective optimisation for generating representative summary clips. Recently, De Leo et al [27] proposed a multicamera video summarization framework which summarizes at the level of activity motif [28]. Due to the severe occlusion, far-field of view and high density activities in surveillance videos, none of the existing techniques solve the problem of distributed multi-scene surveillance video summarization.…”

Section: Multi-scene Understandingmentioning

confidence: 99%

Discovery of Shared Semantic Spaces for Multiscene Video Query and Summarization

Hospedales

Gong

2017

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

The growing rate of public space CCTV installations has generated a need for automated methods for exploiting video surveillance data including scene understanding, query, behaviour annotation and summarization. For this reason, extensive research has been performed on surveillance scene understanding and analysis. However, most studies have considered single scenes, or groups of adjacent scenes. The semantic similarity between different but related scenes (e.g., many different traffic scenes of similar layout) is not generally exploited to improve any automated surveillance tasks and reduce manual effort. Exploiting commonality, and sharing any supervised annotations, between different scenes is however challenging due to: Some scenes are totally un-related -and thus any information sharing between them would be detrimental; while others may only share a subset of common activities -and thus information sharing is only useful if it is selective. Moreover, semantically similar activities which should be modelled together and shared across scenes may have quite different pixel-level appearance in each scene. To address these issues we develop a new framework for distributed multiple-scene global understanding that clusters surveillance scenes by their ability to explain each other's behaviours; and further discovers which subset of activities are shared versus scene-specific within each cluster. We show how to use this structured representation of multiple scenes to improve common surveillance tasks including scene activity understanding, crossscene query-by-example, behaviour classification with reduced supervised labelling requirements, and video summarization. In each case we demonstrate how our multi-scene model improves on a collection of standard single scene models and a flat model of all scenes.

show abstract

Multicamera video summarization and anomaly detection from activity motifs

Cited by 20 publications

References 13 publications

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

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

Diversity-Aware Multi-Video Summarization

Discovery of Shared Semantic Spaces for Multiscene Video Query and Summarization

Contact Info

Product

Resources

About