A novel sequence representation for unsupervised analysis of human activities
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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

A Novel Sequence Representation for Unsupervised Analysis of Human Activities

Presented by: Wei Pan

For CS88/188



Length
Length Human Activities

  • A 40-page paper.

  • Straight-forward way of thinking of a problem.

    • No graph model, no inference, no fancy math.


Definition
Definition Human Activities

  • 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.


Definition1
Definition Human Activities

  • n-gram Histogram

    • An Activity could be represented by a subset of its sequence.


Definition2
Definition Human Activities

  • 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.


Unsupervised classification
Unsupervised Classification Human Activities

  • Distance Measurement.

  • Clustering Algorithm.

  • Cluster Modeling.


Problem 1
Problem #1 Human Activities

  • Distance between two activities?

    • Y, Z are events in A and B respectively. K is normalization factor.


Problem 2
Problem #2 Human Activities

  • Clustering Algorithm

    • A max clique is a class.

    • Dominant set algorithm. ([Pavan2003])


Problem 3
Problem #3 Human Activities

  • Each activity is one of the two types:

    • Regular

    • Anomalous

  • Each class has typical nodes.

    • Calculated through [Kleinburg99]


Problem 4
Problem #4 Human Activities

  • How to understand anomalous activities in a class?


Ups load dock human data
UPS Load Dock Human Data Human Activities


  • 1 month, Human Activities9am-5pm, 5 days a week

  • 61 events, 10 key objects

  • 195 activities, 150 labelled

    • 7 major classes detected. (Table 1)


Residential house sensor data
Residential House Sensor Data Human Activities

  • 5 months

  • 16 Strain gages

  • 16 event

  • every day is an activity


Residential house sensor data1
Residential House Sensor Data Human Activities

  • People seems to have different plans for different day.

    • 5 classes mined out. (Table 2)


Kitchen vision data
Kitchen Vision Data Human Activities



Anomalous analysis works
Anomalous Analysis Works detected?

  • Discover some anomalous activities

    • Truck left with door open

    • Someone cleaning up the floor


Activity class characterization

Activity-Class Characterization detected?

Presented by: Wei Pan

For CS88/188



  • Find a sequence of events detected?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.



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