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
  • A 40-page paper.
  • Straight-forward way of thinking of a problem.
    • No graph model, no inference, no fancy math.
definition
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.
definition1
Definition
  • n-gram Histogram
    • An Activity could be represented by a subset of its sequence.
definition2
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.
unsupervised classification
Unsupervised Classification
  • Distance Measurement.
  • Clustering Algorithm.
  • Cluster Modeling.
problem 1
Problem #1
  • Distance between two activities?
    • Y, Z are events in A and B respectively. K is normalization factor.
problem 2
Problem #2
  • Clustering Algorithm
    • A max clique is a class.
    • Dominant set algorithm. ([Pavan2003])
problem 3
Problem #3
  • Each activity is one of the two types:
    • Regular
    • Anomalous
  • Each class has typical nodes.
    • Calculated through [Kleinburg99]
problem 4
Problem #4
  • How to understand anomalous activities in a class?
slide16

1 month,9am-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
  • 5 months
  • 16 Strain gages
  • 16 event
  • every day is an activity
residential house sensor data1
Residential House Sensor Data
  • People seems to have different plans for different day.
    • 5 classes mined out. (Table 2)
slide20

Whether activities like making salad, washing dishes will be detected?

    • Yes, with a proper n
    • 90% percent accuracy
anomalous analysis works
Anomalous Analysis Works
  • Discover some anomalous activities
    • Truck left with door open
    • Someone cleaning up the floor
activity class characterization

Activity-Class Characterization

Presented by: Wei Pan

For CS88/188

slide23

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.

slide25

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.
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