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A Novel Sequence Representation for Unsupervised Analysis of Human Activities

A Novel Sequence Representation for Unsupervised Analysis of Human Activities. Presented by: Wei Pan For CS88/188. The Unsupervised Activity Classification System. Length . A 40-page paper. Straight-forward way of thinking of a problem. No graph model, no inference, no fancy math.

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A Novel Sequence Representation for Unsupervised Analysis of Human Activities

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  1. A Novel Sequence Representation for Unsupervised Analysis of Human Activities Presented by: Wei Pan For CS88/188

  2. The Unsupervised Activity Classification System

  3. Length • A 40-page paper. • Straight-forward way of thinking of a problem. • No graph model, no inference, no fancy math.

  4. Definition • Key Object • Fridge, washer, stove, sink… • Event • Interaction among a subset of key objects in a certain time. (turn stove on; eat egg; fry egg) • Activity • A sequence of events with temporal order. • Activity Structure • The event sequence of an activity.

  5. Definition • n-gram Histogram • An Activity could be represented by a subset of its sequence.

  6. Definition • Is n-gram real work? • Under certain assumption it works! • Simulation with VMMC. • VMMC: A sampling method (in this paper) to generate sequences of different classes with noise.

  7. Unsupervised Classification • Distance Measurement. • Clustering Algorithm. • Cluster Modeling.

  8. Problem #1 • Distance between two activities? • Y, Z are events in A and B respectively. K is normalization factor.

  9. Problem #2 • Clustering Algorithm • A max clique is a class. • Dominant set algorithm. ([Pavan2003])

  10. Problem #3 • Each activity is one of the two types: • Regular • Anomalous • Each class has typical nodes. • Calculated through [Kleinburg99]

  11. Problem #4 • How to understand anomalous activities in a class?

  12. UPS Load Dock Human Data

  13. 1 month,9am-5pm, 5 days a week • 61 events, 10 key objects • 195 activities, 150 labelled • 7 major classes detected. (Table 1)

  14. Residential House Sensor Data • 5 months • 16 Strain gages • 16 event • every day is an activity

  15. Residential House Sensor Data • People seems to have different plans for different day. • 5 classes mined out. (Table 2)

  16. Kitchen Vision Data

  17. Whether activities like making salad, washing dishes will be detected? • Yes, with a proper n • 90% percent accuracy

  18. Anomalous Analysis Works • Discover some anomalous activities • Truck left with door open • Someone cleaning up the floor • …

  19. Activity-Class Characterization Presented by: Wei Pan For CS88/188

  20. Assume some activities in class c • 1-2-3-4-5 • 3-1-2-3-7 • 3-4-1-2-3-6-4-5 • 5-1-2-3-9-1 It seems 1-2-3 is very common in this class.

  21. Find a sequence of events s, so that s will have a certain prediction power in all activities of class c. Thus s will be a motif of class c. • Prediction power is analytically described as a bit-gain.

  22. Discussions and Questions

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