Associative Tracking and Recognition in Video

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TypeArticle
ConferenceThe First International Workshop on Video Processing for Security (VP4S-06), June 7-9, 2006., Québec City, Québec, Canada
AbstractDue to limited resolution and quality of surveillance video, tracking and recognizing of objects in surveillance video requires techniques for accumulation of information about the object over time. The simplest of these techniques is histograms, which computes the distribution of pixel values over time and which is frequently used for tracking uniformly coloured objects such as faces. Another well-known technique for the purpose is correlograms, which learns pixel values and their spatial relationship to yield better discriminative power. However, this technique also lacks a comprehensive learning ability, because it updates the information about the object, expressed in terms of cooccurance matrices, using the currently observed pixels only and ignoring the preceding learning history. Associative neural network based memorization can thus be seen as a more sophisticated mechanism for accumulation of information about an object in a video sequence. This presentation describes how to use the Open Source Associative Neural Network code for tracking and recognition of objects in video. Two demos showing multiple-face tracking and classification in low-resolution video are shown.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48494
NPARC number8913602
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Record identifierff2dc8c4-5d57-4a7b-b58d-d22d0c0a7739
Record created2009-04-22
Record modified2016-05-09
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