Smart surveillance based on video summarization

In recent years, video surveillance technology has become ubiquitous in every sphere of our life. But automated video surveillance generates huge quantities of data, which ultimately does rely upon manual inspection at some stage. The present work aims to address this ever increasing gap between the volumes of actual data generated and the volume that can be reasonably inspected manually. It is laborious and time consuming to scrutinize the salient events from the large video databases. We introduce smart surveillance by using video summarization for various applications. Techniques like video summarization epitomizes the vast content of a video in a succinct manner. In this paper, we give an overview how to use an optimal summarization framework for surveillance videos. In addition to reduce the search time we propose to convert content based video retrieval problem into a content based image retrieval problem. We have performed several experiments on different data sets to validate our proposed approach for smart surveillance.