Portable Roadside Sensors for Vehicle Counting, Classification, And ...
Object Detection Classification, tracking and Counting
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Transcript of Object Detection Classification, tracking and Counting
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Object Detection,
Tracking, Classification,
and CountingSHOUNAK MITRA
ADVISOR: PROFESSOR TAT. S. FU, PHD, P.E.
CIVIL AND ENVIRONMENTAL ENGINEERING DEPARTMENT
UNIVERSITY OF NEW HAMPSHIRE
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Overview
Significance of the project
Use of camera angles
Video Demonstration
Object Detection
Noise and Shadow issues
Tracking
Classification
Counting
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Project Significance: Pedestrian Detection and Counting
Synchronization of the Objects passing
over the bridge and the readings of
strain gauges and accelerometers.
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Clip Obtained from Prof. Bell’s lab
(Travis and Griggs)
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Camera angles obtained from DOT
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Algorithm 5
Read
Video File
Background
Separation/
Foreground
Detection
Foreground
Filtration
Blob
Analysis
Detect
Boxes
Noise and
Shadow
Issues
Classification
and
Counting
Detection Phase
Processing PhaseFinal Phase
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Object Detection FlowVIDEO FRAME FOREGROUND DETECTION
FOREGROUND FILTRATION OBJECT DETECTION AND COUNTING
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Noise Removal
Preprocessing and Thresholdng:
Deleting boxes formed at unexpected
locations
Kalman Filter
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What is Kalman Filter?
A Kalman filter is an optimal recursive data processing algorithm
The Kalman filter incorporates all information that can be provided to it. It processes all available measurements, regardless of their precision, to estimate the current value of the variables of interest
Computationally efficient due to its recursive structure
Assumes that variables being estimated are time dependent
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What does it do?
Predictor: predicts parameter values ahead of current
measurements
Noise Reduction: reduces noise introduced by inaccurate
detections
Tracking: Facilitates the process of association of multiple objects to
their tracks
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Kalman Filter AKA Predictor - Corrector
(1) Project the state ahead
xˆ-k = Axˆk – 1 + Buk – 1
(2) Project the error covariance ahead
P-k = APk – 1 AT + Q
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Measurement Update (“Correct”)
(1) Compute the Kalman gain
K. k = P-kH
T (HP-kH
T + R)–1
(2) Update estimate with measurement zk
xˆk = xˆ-k + Kk(zk – Hxˆ-
k )
(3) Update the error covariance
Pk = (I – KkH )P-k
Time Update State: Responsible for projecting forward in time the current state
and the error covariance estimates to obtain the a priori estimates for the next
time step.
Measure update state: Responsible for feedback, i.e. for incorporating a new
measurement into the a priori estimate to obtain an improved a posteriori
estimate.
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Tracking using Kalman Filter 11
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The Problem of Shadow 12
Object Misclassification
Overlapping of Objects
Shadow
Region
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Shadow Detection Flow
YES NO
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Specify
Threshold for
Shadow (Sth)
Get Current
Frame Fn
Store
Background
Frame B
Apply Gaussian
Smoothening
(GB & GFn)
Dn =
B/Fn < 1
Multiply by a
factor > 20
(RDn)
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Shadow Detection FlowBackground in RGB Scale Background in Gray Scale Foreground in Gray Scale
Unfiltered Shadow detection Thresholding of Shadow Filtered Shadow Binary Scale
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Classification
Color coded classification
Centroid lying in the color
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Color Coded Classification and
counting
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DELETED
SHADOW
REGION
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Demonstration Video 17