Enhanced security using video summarization for surveillance system using deep LSTM model with K-means clustering technique

Sharma, Shikha; Goyal, Dinesh

doi:10.47974/JDMSC-1778

Open Access Research Article

Enhanced security using video summarization for surveillance system using deep LSTM model with K-means clustering technique

^*Shikha SharmaCorresponding authorshikha.sharma@poornima.edu.inDepartment of CSERajasthanPoornima University, JaipurIndia0009-0000-8736-3936View full profile → , Dinesh Goyaldinesh.goyal@poornima.orgDepartment of CSERajastshanPoornima University, JaipurIndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 913–925Vol. 26Issue 3April 2023DOI: 10.47974/JDMSC-1778 Crossmark XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1778
Pages:: 913–925

Abstract

For the security of the Society, Video surveillance systems have become an important tool that can help in preventing crime in public or private establishments, leading to more safe and secure environment. With the rise of technology that can handle high volune video data and the need for continuous monitoring for security to citizens, the analysis of the video footages, during security breach attempts has become more challenging. Video summarization can play a crucial role in secure surveillance systems by optimizing the analysis time of the security breach. This research attempts to summarise the important aspects of the original video without losing its context and also optimizes the storage requirement. To achieve the same a unique KM-LSTM based technique has been developed that utilizes deep learning methods to understand links among highlight as well as non-highlight video segments through the use of pairwise distance calculations. A new Deep LSTM model has been used to extract the features from all segments of frames. These extracted features are clustered using k-means clustering. Then, such features are trained using a pairwise deep learning model to get highlight scores as well as leverage the comparative similarity among pairs of the highlight as well as non-highlight segments. At last, video summarization takes place to generate the video summary from the highlight segments only. From the results of these experiments discovering the moments of the user’s major or special interest (namely, highlights) in a video, to generating summarization of videos. The above technique can help in increasing the analysis of Video content and thus achieving a more vigilant and secure society.

Keywords

Security Video summarization Deep learning LSTM TVSum50 K-means clustering Pairwise distance calculation Surveillance systems

Subject Classifications

68M2068T07

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