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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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The Recognition of Human Movement Using Temporal Templates
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Machine Vision and Applications - Special issue: IEEE WACV
Framework for Synthesizing Semantic-Level Indices
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Local Abnormality Detection in Video Using Subspace Learning
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This paper presents OS-Guard(On-Site Guard), a novel on-site signature based framework for multimedia surveillance data management. One of the major concerns in widespread deployment of multimedia surveillance systems is the enormous amount of data collected from multiple media streams that need to be communicated, observed and stored for crime alerts and forensic analysis. This necessitates investigating efficient data management techniques to solve this problem. This work aims to tackle this problem, motivated by the following observation, more data does not mean more information. OS-Guard is a novel framework that attempts to collect informative data and filter out non-informative data on-site, thus taking a step towards solving the data management problem. In the framework, both audio and video cues are utilized by extracting features from the incoming data stream and the resultant real valued feature data is binarized for efficient storage and processing. A feature selection process based on association rule mining selects discriminant features. A short representative sample of the whole database is generated using a novel reservoir sampling algorithm that is stored onsite and used with an support vector machine to classify an important event. Initial experiments for a Bank ATM monitoring scenario demonstrates promising results.