Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization

TL;DR

This paper introduces a novel framework for multi-scene video analysis that clusters related scenes and identifies shared activities, enhancing surveillance tasks like activity understanding, querying, and summarization by leveraging semantic similarities across scenes.

Contribution

The work presents a new distributed multi-scene understanding framework that clusters scenes based on behavior explanation and discovers shared versus scene-specific activities, improving surveillance performance.

Findings

Improved scene activity understanding across multiple scenes.

Enhanced cross-scene query-by-example accuracy.

Reduced supervised labeling requirements for behavior classification.

Abstract

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, cross-scene 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.