TOWARDS AN EFFICIENT DISTRIBUTED OBJECT RECOGNITION SYSTEM

8/7/2019 TOWARDS AN EFFICIENT DISTRIBUTED OBJECT RECOGNITION SYSTEM

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TOWARDS AN EFFICIENTTOWARDS AN EFFICIENT

DISTRIBUTED OBJECTDISTRIBUTED OBJECTRECOGNITION SYSTEM INRECOGNITION SYSTEM IN

WIRELESS SMART CAMERAWIRELESS SMART CAMERA

NETWORKSNETWORKS

Nikhil Naikal, Allen Y. Yang, and S. Shankar SastryNikhil Naikal, Allen Y. Yang, and S. Shankar Sastry

Department of EECS, University of California, Berkeley, CADepartment of EECS, University of California, Berkeley, CA

AMRIT JAISWAL

706/IT/071


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INTRODUCTIONINTRODUCTION

y Distributed object recognition is a fast-growing research topic mainly motivatedby portable camera devices and their

integration with modern wireless sensornetwork technologies

y It can be summarised in 3 related areas:

1. Development of smart camera platform.

2. Extraction of dominant image features.

3. Correspondence and compression ofimage features extracted.

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PROPOSED WORKPROPOSED WORK

y The paper proposes an efficient

distributed object recognition system for

recognition of 3-D objects using a

network of wireless smart cameras.

y If a common object is observed by

multiple cameras from different vantage

points, the corresponding features can beefficiently retrieved.

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y For this purpose the authors have

proposed and formulated:1. Berkeley Multi-view wireless database.

2. Joint Sparsity Model.

Distributed Encoding of JS signals. Decoding of Sparse signals.

3. Multiple view Object recognition using a

hierarchical Vocabulary Tree.

4. Experiment and results.

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BERKELEY MULTIBERKELEY MULTI--VIEWVIEW

WI

RELESS DATABASE.WI

RELESS DATABASE.

y The BMW database consists of multiple-

view images of 20 landmark buildings on

where 16 different vantage points have

been selected to measure the 3-D

appearance of the building.

y Its purpose is to aid peer evaluation ofdistributed object recognition methods

for the wireless surveillance scenario.

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y The database provides a two-tier multiple-

view relationship to systematicallybenchmark the performance of multiple-view object recognition algorithms asshown in figure (a) and(b).

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JOINT SPARSITY MODELJOINT SPARSITY MODEL

y The flowchart for the JS Model used for

object recognition is shown below:

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Multiple view Object recognitionMultiple view Object recognition

using a hierarchical Vocabulary Tree.using a hierarchical Vocabulary Tree.y The authors have proposed an efficient

multiple-view object recognition

algorithm that takes multiple-view

features as the input, and outputs a labelas the classification of the object in 3-D.

y The joint classification algorithm is

employed to recover a label of the objectthat minimizes the multiple-view

relevance score i.e., relates it to the

closest match in the vocabulary tree of

the training set . 8


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EXPERIMENT AND RESULTSEXPERIMENT AND RESULTS

y Based on the BMW database, the authorscompare how discriminative three existing robustfeature descriptors are in representing the imageappearance of objects, namely SIFT [1], SURF [2],

and CHOG [3] on two testing scenarios :1. Small-baseline scenario.

2. Large-baseline scenario.

[1] SIFT :Object recognition from local scale-invariant features.

[2] SURF: Speededup robust features.

[3]CHOG: Compressed histogramofgradients a low bit-ratefeature descriptor.

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SMALLSMALL--BASELINE SCENARIOBASELINE SCENARIO

Overall, CHOG features yield the bestrecognition rates compared to the other twofeature descriptors. The authors find this totheir benefit, as CHOG features have beendesigned for distributed wireless camera

application. 10


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y We observe that, with small projection dimensionsclose to 1000, the recognition rates using two or

three cameras improves significantly compared to thesingle-view recognition rates.

y It is also important to note that the improvedrecognition rates using the multiple-view informationare also higher than merely increasing the projectiondimension in the single-camera scenario.

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LARGELARGE--BASELINE SCENARIOBASELINE SCENARIO

y The result demonstrates that multiple large-

baseline images contain much more informationabout a common object in 3-D than a set ofsmall-baseline images.

y Specifically, there is about 10% improvement in

the recognition rates in the 3-camera case. 12


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y As shown in the plot, the recognition rates at the lowprojection dimension of 1000 are lower than those of the

small-baseline scenario for the 2 and 3-cam cases.y However, as the projection dimension increases, the multiple-

view recognition rates reach about 95% and begin to plateau.Such rates are never achieved even without randomprojection in the single view case.

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CONCLUSION ANDCONCLUSION AND

F

UTURE WORKF

UTURE WORKy The authors have presented a framework tojointly classify objects observed from multiplevantage points in a distributed wireless cameranetwork where the multiple-view information of

the object is available to boost the globalrecognition.

y The best recognition rate based on the images of

the 20 landmarks is about 95%. To successfullydeploy such systems in real-world surveillanceapplications, the recognition rates have to beimproved dramatically (e.g., > 99%).

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THANK YOU

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TOWARDS AN EFFICIENT DISTRIBUTED OBJECT RECOGNITION SYSTEM