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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
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab2 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
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab3 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
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab4 Problem Statement Given: a set {S A i } of accelerometer signals and a set {S C j } of tracks extracted from a camera network Find: the match matrix Λ which globally maximizes the similarity between pairs of signals S A i and S C j Main assumptions: Tracker: provides correct tracks in segments ≳ 4 steps Camera placement: oblique from top (typical CCTV) Occlusions: short-lived 01 10 00 Λ = tracks accelerometers
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab5 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
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab6 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…
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab7 Timestamps of Gait Landmarks Idea: Compare timestamps of heel-strike and midswing moments of gait: H = (t H 0, t H 1, … ) M = (t M 0, t M 1, … ) From accels., and cameras: S A i = {H A i, M A i } S C j = {H C j, M C j } Next step: define time-noise independent metric (offset and jitter)
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab8 Distance metric Define distance from timestamp to sequence: Then from sequence to sequence: Then two metrics describing time offset and jitter:
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab9 Global Optimization Invariance to time offset, timestamp noise Global Optimization
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab10 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:
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab11 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
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Thiago TeixeiraYale ENALAB - http://www.eng.yale.edu/enalab12 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
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❖ Thank you. Questions? BehaviorScope: http://www.eng.yale.edu/enalab/behaviorscope.htm Videos: http://enaweb.eng.yale.edu/drupal/PEM-ID-videos
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