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[ITSC2019] Design of Acoustic Vehicle Detector with Steady-Noise Suppression

[ITSC2019] Design of Acoustic Vehicle Detector with Steady-Noise Suppression

Presented in IEEE ITSC 2019, Auckland, New Zealand

S. Ishida, M. Uchino, C. Li, S. Tagashira, and A. Fukuda
Design of Acoustic Vehicle Detector with Steady-Noise Suppression
IEEE International Conference on Intelligent Transportation Systems (ITSC), Auckland, New Zealand, pp.2848-2853, Oct 2019

paper: https://doi.org/10.1109/ITSC.2019.8917289
pdf: https://pman0214.netlify.app/static/69571d72230c4f1063fee9f322e5153e/ishida19-itsc.pdf

Shigemi ISHIDA

October 30, 2019
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  1. Shigemi Ishida1, Masato Uchino1, Chengyu Li1,
    Shigeaki Tagashira2, and Akira Fukuda1
    Kyushu University1, Kansai University2
    ITSC 2019
    Design of Acoustic Vehicle Detector
    with Steady-Noise Suppression
    1

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  2. Background
    2
    [1] N. Buch et al., “Vehicle localisation and classification in urban CCTV streams”, ITS World Congress (2009)
    Motorbike
    detection
    problem
    nVehicle detection is one of the core
    technologies in ITS
    nExisting vehicle detectors
    • Intrusive: Loop coils, photoelectric tubes
    →Require roadwork
    • Non-intrusive : Laser, ultrasound
    →Installed above roads, high cost
    • CCTV-based [1]
    →Low accuracy in bad weather
    →Low-cost high accuracy vehicle detectors

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  3. L
    R
    Acoustic Vehicle Detector(1/2)
    nDetect vehicle sound[2]
    • Estimate direction of sound source using 2 mics and calculate
    sound delay on 2 mics
    n Sound map
    • A map of time difference of vehicle sound
    3
    L R
    Sound map
    [2],S. Ishida, J. Kajimura, M. Uchino et al., “SAVeD: Acoustic vehicle detector with speed estimation capable of sequential vehicle detection,”
    in Proc. IEEE Conf. Intelligent Transportation Systems (ITSC), Nov. 2018, pp. 906–912.

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  4. Acoustic Vehicle Detector(2/2)
    nCross-correlation function
    • Maximum at t=∆t for signals with delay of ∆t
    • Find peak of cross-correlation function
    • We used GCC (generalized cross-
    correlation function)
    nRANSAC
    • Robust estimation method for model fitting
    • Fit S-curves to the sound map
    to detect passing vehicles
    4
    Peak
    0.8ms
    = $ !
    "
    + s1(t), s2(t): sound on the mics

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  5. Issue
    nHow to detect vehicles in the steady noise condition?
    • Rain causes steady noise on the two microphones
    • The steady noise generates a noise peak and weaken a vehicle sound peak
    5
    −1.0 −0.5 0.0 0.5 1.0
    Sound delay [ms]
    −0.04
    −0.03
    −0.02
    −0.01
    0.00
    0.01
    0.02
    0.03
    0.04
    0.05
    GCC
    -1
    -0.5
    0
    0.5
    1
    320 325 330 335 340 345 350 355 360
    Sound Delay ∆t [ms]
    Time t [s]
    Incorrect
    sound-delay
    estimation
    Noise points make
    difficult to detect

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  6. nMathematically exclude the influence of steady noise
    in a sound delay estimation
    • Formulate the noise influence
    • Store and update the noise data
    →We perform signal processing on the stored noise to move
    GCC noise peaks out of the sound map
    6
    Key Idea

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  7. Design Overview
    1. Retrieve sound on 2 microphones
    2. Estimate a vehicle passing in the probabilistic vehicle detector.
    3. Update the noise data in Noise Storage when no vehicle is passing
    4. Remove noise peaks by performing signal processing
    5. Draw sound map and detect vehicles using RANSAC Fitting
    7
    Signal processing
    FFT
    Probabilistic
    Vehicle
    Detector
    LPF
    LPF
    FFT Noise
    Storage
    Sound
    Mapper
    Vehicle
    Detector
    Sound Retriever
    Noise Updater
    Thresholding

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  8. Steady-Noise Suppression
    nMathematical approach
    • Control GCC by performing signal processing and changing stored noise phase
    nLet V and N and "
    be vehicle sounds, noise and stored noise
    • !
    ! =
    ! +
    ! + !
    • !
    "
    =
    "
    +
    "
    +#$"%&' "

    lWhen no vehicle is passing ( ≈ !
    )
    • GCC peak is dominated by stored noise phase
    lWhen a vehicle is passing (V ≫ !
    )
    • GCC peak corresponds to vehicle sound delay ∆
    8
    -1
    -0.5
    0
    0.5
    1
    320 325 330 335 340 345 350 355 360
    Sound Delay ∆t [ms]
    Time t [s]
    -1
    -0.5
    0
    0.5
    1
    320 325 330 335 340 345 350 355 360
    Sound Delay ∆t [ms]
    Time t [s]
    ( > 1)
    signal processing

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  9. nUpdate the noise data in the noise storage
    • Rain-noise frequency components change over time
    →We control the noise by switching the noise update timing
    • We employ our ultra low-power vehicle detection method[3]
    Noise Updater
    9
    Sound
    Delay
    On
    Noise
    Update
    Switch Off
    Probability
    of Vehicle
    Passing
    Time
    Threshold
    Sound
    Delay
    robability
    of Vehicle
    Threshold
    Sound
    Delay
    Probability
    of Vehicle
    Threshold
    [3] K. Kubo, C. Li, S. Ishida et al., “Design of ultra low power vehicle detector utilizing discrete wavelet transform,” in Proc. ITS AP Forum, May 2018, pp. 1052–1063.

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  10. Experiment Environment
    nVehicle sound collected on camp
    ⦁ 2-lane road (1-lane in each dir)
    ⦁ 48kHz, 16bit, Mic separation D=30cm
    ⦁ AZDEN SGM-990 mic
    ⦁ Sony HDR=MV1 recorder
    ⦁ Record video as ground truth
    nNormal rain condition:
    • 93 vehicles passed
    • Recorded for approximately 23 minutes
    nHeavy rain condition:
    • 165 vehicles passed
    • Recorded for approximately 45 minutes
    10




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  11. Normal Rain Condition Heavy Rain Condition
    w/ Noise
    Suppressor
    w/o Noise
    Suppressor
    w/ Noise
    Suppressor
    w/o Noise
    Suppressor
    L to R R to L Total L to R R to L Total L to R R to L Total L to R R to L Total
    Precision 1.00 0.98 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
    Recall 0.77 0.98 0.86 0.67 0.93 0.79 0.79 0.87 0.82 0.57 0.69 0.62
    F-measure 0.87 0.98 0.92 0.80 0.96 0.88 0.88 0.93 0.90 0.73 0.82 0.76
    Experiment Result
    11

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  12. FP Detection Problem
    nFP was caused by sequentially passing vehicles
    • Detection algorithm based on a RANSAC fitting
    →Sequentially passing vehicles cause
    miss fitting
    nSteady-noise suppressor revealed
    sequential vehicle problem
    12
    Time t [second]
    Sound delay [second]

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  13. Summary
    nSteady Noise Suppressor
    • Control GCC by performing signal processing and changing stored noise
    phase
    • Store and update noise data
    • Detector with steady noise suppressor detected vehicles with F-measures
    of 0.92 and 0.90 in normal and heavy rain conditions, respectively.
    nSequential Vehicle Problem.
    • Need to improve detection algorithm for the sequential vehicle problem
    13

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  14. 14
    Thank you!!

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  15. Detect vehicle sound
    Detect vehicle sound
    ◦ Estimate direction of sound source using stereo mics at
    sidewalk
    Advantages
    ◦Monitors multiple lanes at one
    side
    ◦Low deployment cost with low
    height configuration
    ◦Detect all types of vehicles
    15

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  16. Acoustic Vehicle Detector(3/3)
    16
    t
    t
    O
    t
    t
    O
    t
    t
    O
    t
    t
    O
    nRANSAC
    • Robust estimation method for model fitting
    nProcess
    1. Randomly sample a point
    2. Estimate an S-curve based
    on the sampled point
    3. Total distances
    4. Repeat 1)~3) and completefitting
    with the minimum sum

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  17. Steady-Noise Suppression
    nRemove noise mathematically
    • Let V and N and !
    be vehicle sounds, noise and stored noise
    • is noise supplement factor, is noise delay
    • The sound signal GCC -
    .!
    -
    ."
    / is:
    , ,
    nWhen no vehicle is passing ( ≈ !
    )
    nWhen a vehicle is passing (V ≫ !
    )
    17
    ¯
    S1(f) = V1(f) + N1(f) + ¯
    N1(f)
    ¯
    S2(f) = V2(f) + N2(f) + e j2⇡f⌧ ¯
    N2(f)
    h
    RP

    n1
    ||¯
    n2
    |
    (t)
    i
    MAX
    >

    RP
    n1n2
    (t)

    MAX
    (1)

    RP
    v1v2
    (t)

    MAX
    >
    h
    RP

    n1
    ||¯
    n2
    |
    (t)
    i
    MAX
    (1)
    RP
    ¯
    s1 ¯
    s2
    (t) = RP
    v1v2
    (t) + RP
    n1n2
    (t) + RP

    n1
    ||¯
    n2
    |
    (t)
    RP
    v1v2
    (t) =
    Z
    Gv1v2
    (f)
    |G¯
    s1 ¯
    s2
    (f)|
    ej2⇡ft dfRP
    n1n2
    (t) =
    Z
    RP
    v1v2
    (t) =
    Z
    Gv1v2
    (f)
    |G¯
    s1 ¯
    s2
    (f)|
    ej2⇡ft dfRP
    n1n2
    (t) =
    Z
    Gn1n2
    (f)
    |G¯
    s1 ¯
    s2
    (f)|
    ej2⇡ft dfRP

    n1
    ||¯
    n2
    |
    (t)
    RP
    v1v2
    (t) =
    Z
    Gv1v2
    (f)
    |G¯
    s1 ¯
    s2
    (f)|
    ej2⇡ft dfRP
    n1n2
    (t) =
    Z
    Gn1n2
    (f)
    |G¯
    s1 ¯
    s2
    (f)|
    ej2⇡ft dfRP

    n1
    ||¯
    n2
    |
    (t) =
    Z
    G|¯
    n1
    ||¯
    n2
    |
    (f)
    |G¯
    s1 ¯
    s2
    (f)|
    ej2⇡f(t ⌧) df
    ¯
    S1(f) = V1(f) + N1(f) + ¯
    N1(f)
    ¯
    S2(f) = V2(f) + N2(f) + e j2⇡f⌧ ¯
    N2(f)
    -1
    -0.5
    0
    0.5
    1
    320 325 330 335 340 345 350 355 360
    Sound Delay ∆t [ms]
    Time t [s]
    -1
    -0.5
    0
    0.5
    1
    320 325 330 335 340 345 350 355 360
    Sound Delay ∆t [ms]
    Time t [s]
    ( > 1)

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  18. Normal Rain Condition Heavy Rain Condition
    w/ Noise
    Suppressor
    w/o Noise
    Suppressor
    w/ Noise
    Suppressor
    w/o Noise
    Suppressor
    L to R R to L Total L to R R to L Total L to R R to L Total L to R R to L Total
    TP 39 41 80 34 39 73 74 62 136 54 49 103
    FN 12 1 13 17 3 20 20 9 29 40 22 62
    FP 0 1 1 0 0 0 0 0 0 0 0 0
    Precision 1.00 0.98 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
    Recall 0.77 0.98 0.86 0.67 0.93 0.79 0.79 0.87 0.82 0.57 0.69 0.62
    F-measure 0.87 0.98 0.92 0.80 0.96 0.88 0.88 0.93 0.90 0.73 0.82 0.76
    Experiment Result
    18

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  19. © 2019 Shigemi ISHIDA, distributed under CC BY-NC 4.0

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