SPIE DSS 2013 Presentation

8/10/2019 SPIE DSS 2013 Presentation

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Objectives

To detect intrusions on the Energy Pipeline ROW.

To prevent any damage to underground energy pipelines due to heavy weightof vehicles

To detect and classify various types of construction machinery on ROW

To validate algorithms on each of database New Era, ATE, GeoEye, AmericanAerospace

To cross-validate the algorithm between the databases

Backhoe Skid Steer TrencherExcavator Mini-

Excavator

Excavator Backhoe

New Era Dataset

ATE Dataset


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Problem Statement

To establish a novel object detection algorithm which satisfies different

challenges (Real life problems) Low and overly exposed illumination; cast shadows

Different viewpoint, scale and orientation

Varying resolution, motion blur

Purpose: Generalize the algorithm parameters for all the datasets

Developed with the mindset for implementation on GPUs and multi-coreprocessor.

Have a deployable real-time system which works on the fly

Low

Illumination

Cast

Shadows

Different

Viewpoint

Motion BlurHigh

Illumination

Different

Orientation

Different

Scale

GTX-680 1532

cores


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Characteristics of Local Phase Domain

Analysis is done local phase domain to tackle the issue of non-uniform

illumination. This local phase will characterize a construction equipment from the

surrounding background(trees, buildings etc..)

It is illumination invariant

Not affected by over exposure to lighting, very low illumination conditions

Brings out the edges/corners of the machine

Backhoe Flight 8 Local Phase Backhoe Flight 6 Local Phase


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Constraints of Local Phase Domain

Rotation/Scale variant

requires a preliminary stage to get a shortlist of possible object regions ofsuitable scale and orientation

View point variant

a global descriptor feature set which is partially invariant to viewpointchanges ( global histogram of the local phase values in a region)

Motion blurring and varying resolution Using multi-resolution image representation to extract local phase

New Era Dataset ATE Dataset


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METHODOLOGY


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Training

Generation of Log-Gabor filters for local phase computation.

Computation of local phase of template using the Log-Gabor filters to

create frequency scale-space representation.

Will account for some amount of resolution changes.

Extension to multiple template sizes (scale) Will account for objects with different sizes

Local

Phase

Template Selection

Local

Phase


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Local Phase based Template Matching

Multi-Scale Multi-Orientation Matching of test image with template in

local phase domain

Matching of template at multiple rotations of image using normalized cross

correlation.

Select the most optimal match at each orientation using a global histogram

matching technique at each rotation of image.

Template

Matching

Matching at different

Orientation

Test Image Search Region


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Orientation Selection and Cluster Voting

Selection of the correct orientation within a search region

Compute local phase matching for every scale and frequency band to get aset of detections for the single selected orientation.

Hierarchical clustering to find groups of detection points within a searchregion.

Apply Voting Mechanism

Each detection in a group/cluster will be assigned a vote based on the Earthmovers distance between template and region. (Green highest vote)

Select

Orientation

Voting

Scheme

Multi-Scale Multi-

Orientation Matching

Single Orientation Multi-

Scale Detections

Voting


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Detection using HOP

Automatic selection of cluster groups in whole image with minimum

number of votes.

Compute Histogram of Oriented Phase (HOP) for each detection.

Compare HOP descriptor with that of template and compute number of

HOP hits for selected cluster groups.

Retain the cluster groups with minimum number of HOP hits.

HOP Matching

Selected Cluster Group Final Detection Zoomed in View of Final

Detection

Zoomed

in View


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DATASET ILLUSTRATIONAND RESULTS


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Image From New Era Dataset


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Image from GeoEye Dataset


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Image from AAAI dataset


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Statistics ( New Era Dataset)

Equipment/Stages Stage1 Stage 2 Stage 3 False positives

Backhoe (Flight)

1(2) Y Y Y 0

2(3) Y Y Y 0

3(4) Y X X

4(5) Y Y Y 1

5(6) Y Y Y 0

6(7) Y Y Y 1

7(8) Y Y Y 0

Total False Positives 2

True Detection Rate 100.00% 85.71% 85.71%

False Detection Rate 5.88%


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Statistics ( Geo Eye)Equipment/Stages Stage1 Stage 2 Stage 3 False positives

ATV

1 Y Y Y 3

2 Y Y Y 1

3 Y Y Y 4

4 Y Y Y 0

5 Y Y Y 1

6 Y Y Y 0

7 Y X X

8 Y Y Y 6

9 Y Y Y 0

Total False Positives 15

True Detection Rate 100% 88.9% 88.9%

False Detection Rate 30.61%


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Reason for Non-detection

Overexposed lighting on the equipment which washes out the

features on the object required for detection.

Possible solution:- Non-linear enhancement of the region of

interest and super-resolution of region to get back the features.

Flight 3 Backhoe in successive frames

Flight 1

Backhoe

(Training Set)


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FUTURE WORK


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Removal of Sensor Noise

Noise due to the sensor properties

Will contain artifacts which can interfere with object pattern.

Gaussian

Filtering(Specific

Parameters)


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Multi-Resolution Image Space Representation

Use a multi-resolution image space representation for computing local phase.

Previous

Method

New

Method


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Kernel-based Local Phase Density Estimation

Instead of computing histogram of phase, we use a Gaussian Kernel to estimate

actual local phase density.

Histogram of Phase Descriptor for Backhoe


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Kernel-based Local Phase Density Estimation

Advantage: Improves the detection as we care comparing true density

distributions

Local Phase Density computed using Kernel.

(Left : no binning, Right: Binned version)


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Thank You

Questions?

SPIE DSS 2013 Presentation

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