Download Attendee Introduction

Sensor network based vehicle
classification and license plate
identification system
Jan Frigo, Vinod Kulathumani
Ed Rosten, Eric Raby
Sean Brennan
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 1
Scenario
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Facility monitoring
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Vehicle classes
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detect suspicious vehicles entering secure area
deployed at key access points / check posts or along length of a road
Personal such as car, SUV
Heavy loads such as pickup trucks,
Military vehicles such as ATV, hummers and huge log trucks
Platforms
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Mica2 motes
ARM processor stargates
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Objectives
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Classify vehicles with
• High reliability
• low latency
• low energy
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Extract license plate image
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 3
Challenges
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Small scale deployment at each access point (< 10 units)
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Vehicles last in influence region for a short time (1-2 seconds)
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Spectral signature of vehicles changes with time
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Resource constrained devices
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Vehicles moving at variable speeds
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Environment
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Temperature
Physical barriers (trees, winding road, environmental)
System Power
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 4
Seismic Acoutic Node Architecture
Network
2 GHz
900 MHz
Mica2
Geophone
Stargate
Microphone
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 5
Seismic-Acoustic Field Experiment
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Frequency characteristics of seismic detection

Geophone placed 50 ft away from road to avoid acoustic
interference
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 7
Seismic detection
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Sample at 100 Hz
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Estimate ‘energy’ of 12-25Hz band
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Haar wavelets up to level 2
Energy = average of coefficients of band 2
Haar wavelet computed on 128 samples every 10 ms
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16 bits samples using MDS320 board
10 new samples each round
118 samples from previous round
Compute variance of the energy on moving window of size 20
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Use variance threshold to detect vehicle
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 8
Seismic detection performance
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Seismic detection triggers acoustic sampling and / or
processing
• Energy efficient
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Person walking 2 ft does not trigger detection
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Person thumping feet (running) < 10 ft away triggers
detection
• Can be isolated using temporal characteristics
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 9
Acoustic classification
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FFT used to obtain spectral characteristics
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Fixed point FFT implemented on stargate
Classifier trained using FFTs computed on stargate
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Identify best feature vector characteristics to distinguish
between vehicle classes
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Use Fisher linear discriminant analysis (FLDV) for classification
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Pairwise classifier
Select order of classification that maximizes accuracy
Input obtained vectors into stargate for classification
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 10
Acoustic classification
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FFT computed every 1/8 of a second
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Consider frequencies > 64 Hz
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512 samples FFT
12 samples from previous round
8Hz resolution
Mic response varies at < 60 Hz
Temporal variation in response < 60 Hz (probably due to wind)
Closest 1.5 seconds of data used as training samples
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 11
Mean FFT coefficients for Vehicle Classes
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 12
Acoustic classification
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Classification order that maximizes accuracy
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Presence of vehicle
Hummer vs car and truck
Car vs truck
Presence
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Use average ‘energy’ of 200-360 Hz band
Moving window variance (size 20) based detection
200-360 Hz band less sensitive to high frequency chirp and wind noise
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 13
Classification using FLDV
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Hummer vs car / truck
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Feature vector 1: ratio of
energies of 80 – 112 Hz and
350 – 500 Hz bands
Feature vector 2: ratio of
energies of 250 – 300 Hz
and 350-500 Hz bands
Ratios less sensitive to mic
response and distance from
road
FLDV uses training
samples to compute best
projection vector
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Acoustic classification

Integrating output
• Approaching vehicle characteristics differ from closest point
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Classifier operates for ~10 seconds as the vehicle approaches and
passes node
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Classifier designed such that
— Low probability of car being classified as truck or hummer at any
instant
— Low probability of truck being classified as hummer at any instant
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> 5 consecutive truck classifications within a test run-> vehicle is truck
> 5 consecutive hummer classifications within a test run -> vehicle is
hummer
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 15
License Plate Recognition
Classify Pixels
Find bounding boxes
Find and
filter
regions
Extract and
resample plates
Send image
Sending only license plates over the network requires very little bandwidth.
Resampling license plate to fixed size reduces network usage when vehicles are
close.
Computationally expensive OCR is run on remote host
Algorithms use integer arithmetic only

UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 16
Performance
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Vehicle classification
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Within 2 seconds of object passing zone
Reliability > 90%
2.26 watts power consumption [when computing]
Will last about 12 hours if continuously computing on 4.8V 4200mAH cell
License plate recognition
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Detection accuracy > 95%
Suppresses 90% of image content
Latency 5.1 seconds [mainly for image capture]
Higher power consumption [can perform 5582 trials on 4.8V 4200maH cell]
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
License plate detection algorithm
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Combination of
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Analyse using Haar Wavlet like features
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Viola jones detector [object detection]
Decision tree classifier [license plate segmentation]
Efficient to compute using an integral image
Integral image is also required for resampling license plate
Computational time is independent of feature size
Train decision tree classifier to recognize license plate pixels
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Decision trees are very efficient:
— Only integer arithmetic required for evaluation
— Tune the tree to rapidly reject most pixels very quickly
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 18
Ongoing and Future Work
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Increase power efficiency
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Embedded FPGA implementations for in situ computing
Estimated 5 – 100x power savings and 30 – 100x speed up in run-time
performance over COTS
Node Architecture combination of
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new ARM processor technology on next generation mezzanine board
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Igloo FPGA on sensor board
Low power analog acoustic circuitry
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Design of cooperative analog-digital signal processing systems
Upto 300X power savings
Identify optimal balance between nodal computation, in-network processing and
central computation
UNCLASSIFIED
Operated by Los Alamos National Security, LLC for NNSA
Slide 19