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Location Based Activity Recognition Lin Liao, Dieter Fox, Henry Kautz. In Adv. in Neural Information Processing Systems, 2005. SELECT lab meeting: Jonathan Huang ( jch1@cs.cmu.edu ) Advisor: Carlos Guestrin 4/25/2006. Task. Given GPS location data we would like to:
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Location Based Activity RecognitionLin Liao, Dieter Fox, Henry Kautz.In Adv. in Neural Information Processing Systems, 2005. SELECT lab meeting: Jonathan Huang (jch1@cs.cmu.edu) Advisor: Carlos Guestrin 4/25/2006
Task • Given GPS location data we would like to: • Segment a user’s day into activities • “Working” • “Visiting” • “Travelling” • Label significant locations associated with one or more activities • “Workplace” • “Friend’s House” • “Bus stop”
Applications • Automated Diary • Long term health monitoring
Previous Approaches • To identify significant locations • Use simple temporal threshold • HMMs, DBNs (generative) • This approach will be discriminative
Outline • An Example Activity Model • Conditional Random Fields • Relational Markov Networks • The Location-based Activity Model • Efficient Inference • Summation templates • FFT-based Belief Propagation • Experimental Results