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Preserving Interactions among Moving Objects in Surveillance Video Synopsis

Publication Type : Journal Article

Publisher : Springer

Source :  Springer

Url :

Campus : Amritapuri

School : School of Computing

Center : Computer Vision and Robotics

Year : 2020

Abstract : Video synopsis is an effective solution for fast browsing and retrieval of long surveillance videos. It aims to shorten long video sequences into its equivalent compact video representation by rearranging the video events in the temporal domain and/or spatial domain. Conventional video synopsis methods focus on reducing the collisions between tubes and maintaining their chronological order, which may alter the original interactions between tubes due to improper tube rearrangement. In this paper, we present an approach to preserve the relationships among tubes (tracks of moving objects) of the original video in the synopsis video. First, a recursive tube-grouping algorithm is proposed to determine the behavior interactions among tubes in a video and group the related tubes together to form tube sets. Second, to preserve the discovered relationships, a spatio-temporal cube voting algorithm is proposed. This cube voting method optimally rearranges the tube sets in the synopsis video, minimizing false collisions between tubes. Third, a method to estimate the duration of the synopsis video is proposed based on an entropy measure of tube collisions. The extensive experimental results demonstrate that the proposed video synopsis framework condenses videos by preserving the original tube interactions and reducing false tube collisions.

Cite this Research Publication : Namitha, K., and Athi Narayanan. "Preserving interactions among moving objects in surveillance video synopsis." Multimedia Tools and Applications (2020): 32331-32360.

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