1. A Forest of Sensors: Classification
MIT Artificial Intelligence Laboratory
A Forest of Sensors: Classification
Chris Stauffer
Tracking over extended periods
Automatic hierarchical classification
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courtesy of MIT Computer Graphics Group
video cameras
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Tracking playback
Query retrieval
Using co-occurence statistics and an individual track as a query, we can return tracks which are similar to a query track in the context of that scene.
Any of our tracking data can be accessed by specifying a time (e.g. Our classifiers and viewers can access any period or find any tracking sequence. Unfortunately, due to lack of resources we can only keep approximately 100 gigabytes of the over 1 terabyte of data we have collected so far.
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Outlier detection
Using our statistical model of activities we can determine that certain activities are abnormal. The tracks which do not fit the activity model are labeled and ranked.
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Long-term activity monitoring
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The cycles of activities which happen over days, weeks, or even longer can be modeled by determining pairwise similarities of different periods of the day and looking for cyclic patterns. The easiest is the daytime/nighttime similarities, but other patterns may occur in factories, schoolyards, military bases, etc.
Minimally supervised classification
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We are currently working to find these patterns to determine when usual activities happen at unusual times or in unusual quantities.
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Another classification method is to use co-occurrence data to boot-strap from a minimum number of examples to create interesting classifiers.
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