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❖
PEM-ID: Identifying People by Gait-Matching using
Cameras and Wearable Accelerometers
Thiago Teixeira, Deokwoo Jung, Gershon Dublon, Andreas Savvides
Yale ENALAB
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 2
Introduction
Can we uniquely identify people in camera networks?(in cooperative enviroments)
Motivation: Assisted Living
identify people in a home Security
locate personnel Corporate environments
track facility usage
Plus, obtaining data traces for research: Yale BehaviorScope project
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 3
Main Idea
Equip each person of interest with a wearable accelerometer node (with known ID)
Extract “motion signature” from: each accelerometer unique ID each track Position
Find pairs of matching signatures to obtain ID+Position
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 4
Problem Statement
Given: a set {SAi} of accelerometer signals and a set
{SCj} of tracks extracted from a camera network
Find: the match matrix Λ which globally maximizes the similarity between pairs of signals SA
i and SCj
Main assumptions: Tracker: provides correct tracks in segments ≳ 4 steps Camera placement: oblique from top (typical CCTV) Occlusions: short-lived
0 1
1 0
0 0
Λ =
tracks
accelerometers
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 5
Challenge: motion signature
Motion paths can be subdivided into two types: Transition motion
Starting, stopping, turning, changing speed Large changes in tangential and centripetal acceleration
Cruising motion Approximately same-speed linear motion Only small-scale changes in acceleration Gait Comprises majority of time
Intuition: to ID people most of the time, use gait Challenge: Nodes are not time-synchronized, have
limited processors and low bandwith
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 6
Correlating Gait Signals from Asynchronous Sources
Sample-oriented methods are unsuitable for WSNs:(eg. Pearson's corr. coefficient, mutual information) Fail given time synchronization offsets (or must slide
one of the signals and recalculate) Require a large number of samples to converge Requires resampling/interpolation if signals have
different sampling frequencies and/or phases
We can do better, using gait frequency and phase…
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 7
Timestamps of Gait Landmarks
Idea: Compare timestamps of heel-strike and midswing moments of gait: H = (tH
0, tH1, … )
M = (tM0, tM
1, … ) From accels., and cameras:
SAi = {HA
i, MAi}
SCj = {HC
j, MCj}
Next step: define time-noise independent metric (offset and jitter)
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 8
Distance metric
Define distance from timestamp to sequence:
Then from sequence to sequence:
Then two metrics describing time offset and jitter:
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 9
Global Optimization
Invariance to time offset, timestamp noise
Global Optimization
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 10
Multiple-Person Simulations
We recorded 24 one-person traces: 12× walking straight in different directions 12× walking and turning in different directions
We overlapped multiple single-person traces with random time offsets (up to 1s) to simulate multiple-person scenarios:
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 11
Three-Person Experiments
Three people walking through FOV One person wearing an accelerometer
Average recognition rate: 87.5%
http://enaweb.eng.yale.edu/drupal/PEM-ID-videos
Thiago Teixeira Yale ENALAB - http://www.eng.yale.edu/enalab 12
Conclusion
Presented a method to ID people in videos using accelerometers
Accuracy > 83%, for up to 10 people + 10 accels
Currently adapting for indoor use Much smaller FOV multiple cameras Occlusions use additional features
❖
Thank you.
Questions?
BehaviorScope:http://www.eng.yale.edu/enalab/behaviorscope.htm
Videos:http://enaweb.eng.yale.edu/drupal/PEM-ID-videos