A Forest of Sensors: Classification MIT Artificial Intelligence Laboratory A Forest of Sensors: Classification Chris Stauffer stauffer@ai.mit.edu http://www.ai.mit.edu/projects/vsam/ Tracking over extended periods Automatic hierarchical classification Technology Square courtesy of MIT Computer Graphics Group video cameras Camera A Camera D Image Template 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. today@8, 9/21/98@12:45:13). 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. 2am 4am 6am 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. 8am 10am Long-term activity monitoring 12pm 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 2pm We are currently working to find these patterns to determine when usual activities happen at unusual times or in unusual quantities. 4pm Day1 Day2 Another classification method is to use co-occurrence data to boot-strap from a minimum number of examples to create interesting classifiers. 6pm Day1 8pm Day2 10pm