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Proceedings ArticleDOI

Surveillance Video Synopsis

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TLDR
A fundamental goal of any video summarization or synopsis technique with reference to a surveillance video is to reduce the Spatio-temporal redundancy, which is removed by detecting the frames having low activity and then deleting those frames.
Abstract
Video is a powerful tool to show various activities but generally we use still images to show a condensed video, which is problematic in viewing and comprehending. Thus, there is a need for a summarized surveillance video. A fundamental goal of any video summarization or synopsis technique with reference to a surveillance video is to reduce the Spatio-temporal redundancy. The activity in any surveillance video is very less as compared to the total length of the video. The spatial redundancy is removed by showing two activities that happened in different frames at different spatial locations in a single frame. Temporal redundancy is removed by detecting the frames having low activity and then deleting those frames. We then generate a stroboscopic video, which traces path of the extracted object. Lastly, we introduce a media Player, which indexes the video synopsis to the original video demonstrating how the video synopsis can be used as an effective tool.

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Citations
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Journal ArticleDOI

Optimization method for trajectory combination in surveillance video synopsis based on genetic algorithm

TL;DR: The method utilizes the temporal combination ways of trajectories to deal with the optimization problem of motion trajectory combination under the idea of genetic algorithm (GA).
Proceedings ArticleDOI

Multilevel Framework for Summarization of Surveillance Videos

TL;DR: This paper presents a mechanism to select salient blocks which can contribute in the video summarization by propagating motion information from frame level to segment level and further to block level and presents the experimental results on surveillance videos.
Proceedings ArticleDOI

Single Camera Surveillance Video Synopsis: A Review and Taxonomy

TL;DR: A comprehensive, categorical review based on single-camera surveillance video synopsis methodologies with a focus to develop an insight into the current research trends and compared and projected in several ways for future research.
Journal ArticleDOI

Detecting Criminal Activities of Surveillance Videos using Deep Learning

TL;DR: A novel approach dealing with Convolutional Neural Networks using Deep Learning was used to sample the priority information from the surveillance videos and the results of the CNN model effectively were able to extract suspicious activity frames from a long video and thus extract suspicious frames and create a video from it.
References
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Proceedings Article

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TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article

A density-based algorithm for discovering clusters in large spatial Databases with Noise

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Journal ArticleDOI

An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis

TL;DR: A novel method for generating key frames and previews for an arbitrary video sequence by first applying multiple partitional clustering to all frames of a video sequence and then selecting the most suitable clustering option(s) using an unsupervised procedure for cluster-validity analysis.
Proceedings ArticleDOI

Making a Long Video Short: Dynamic Video Synopsis

TL;DR: This work presents dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times.
Book ChapterDOI

Background cut

TL;DR: Zhang et al. as mentioned in this paper proposed a real-time foreground layer extraction algorithm based on background contrast attenuation, which adaptively attenuates the contrasts in the background while preserving the contrasts across foreground/background boundaries.
Related Papers (5)
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How to make a synopsis?

The paper discusses the process of creating a surveillance video synopsis by reducing spatio-temporal redundancy, removing spatial redundancy by showing multiple activities in a single frame, removing low activity frames, and generating a stroboscopic video.