Posterpresentationbinder dossick

Page 1

A tomated Assessment of Tornado-Induced Automated Tornado Induced Ind ced Building B ilding Damage g Based on Laser Scanning g Alireza G. Ali G Kashani K h i ((alireza.geranmayeh@gmail.com) g y @g ) Ad i Advisor: Dr. D Andrew A d Graettinger G i g D p t Department t off Ci Civil, il, Construction, C t ti , and d Environmental E i t l Engineering, E gi i g, The Th University U i ity off Alabama Al b Introduction d

Research h Approach pp h

Results l

• Damage assessment after natural disasters is important for • Data processing algorithms forensic engineering and loss estimation. estimation

• Key is to preserve perishable data of damaged buildings. • 3D laser scanning is an effective technology that captures geometric information with high precision.

I Input GIS layers l

• Point cloud of damaged buildings

• Pre-tornado Pre tornado aerial image

• Dense 3D point cloud data produced by laser scanning virtually reconstructs the damage site and enables engineers to take measurements and retrieve geometric i f information. i LLoss off rooff covering Uplift of roof

Failure of large g section of roof

Estimating wind speed based on damage

D processing Data i algorithms l ih

• Filter noise points (ground, trees, etc.) • Identify roof and wall surfaces • Detect Damage • Quantify the extent of damage • Extract geometric information: • Area of roof covering defects • Percentage off sheathing h h lloss • Roof pitch • Spat Spatial a relationships e at o s ps bet between ee bu building d g

O Outputs

• Quantitative damage information

• GIS damage map • GIS G S wind i d speed d map

Correlation of wind speed & roof damage

Automatically generated wind speed map

Extracting i rooff planes l and d their h i spatial i l relationships l i hi to the h tornado and wind direction

surfaces and tornado/wind / direction

• Estimate wind speed based on damage

• Algorithm tests in controlled conditions Angle between the plane normal and tornado path

Failure of d garage door

3D data d t collection ll ti with ith llaser scanners

Failure of some exterior walls

Information extracted for the selected plane: Sheathing loss: 35% Roof pitch: 33 degree Distance: 132 meter Angle:18 Angle: 8 degree

Broken window

3D point i t cloud l d off damaged d d buildings b ildi

• Manually taking measurements and processing data is challenging and time consuming due to the large amount of data collected and the repetitive nature of manual operations and calculations calculations.

• This PhD research aims at developing a data processing

Simulated data

Damaged roof model

Laboratory scans

• Algorithm l i h tests iin reall conditions di i

Detectingg roof coveringg defects Red: Roof shingles Black: Plywood Blue: Roof felt

framework to automatically analyze tornado-induced tornado induced b ildi d building damage based b d on laser l scanning i data. d Source: Sou ce http://extremeplanet.me/ ttp //e t e ep a et e/

Source: Sou ce FEMA at http://fema.maps.arcgis.com/ ttp // e a aps a cg s co /

p – Tuscaloosa,, AL Residential complex

Practical Implications

• Performance data to improve structural analysis, analysis design, design and d construction i

Extracted roof planes and spatial information

g Residential neighborhood – Moore,, OK

Results

• Automatedd ddata processing i fframeworkk for: f Identifying roofs and walls and calculating percentage of loss

Detected roof defect regions

• Sensitivity S i i i analysis l i off framework f k parameter settings i

• Generation of damage maps for disaster response and recovery

• Loss database updating for management of insurance and disaster assistance programs

• Preliminary P li i l loss estimation ti ti ffor costt analysis l i off repair i and d reconstruction projects p j Automatically generated GIS damage maps

Influence of GIS raster cell size on accuracy of (a) roof detection and (b) wall detection

Information Extraction and Automated Reasoning for Automated Regulatory Compliance Checking in the Construction Domain Jiansong Zhang, jzhang70@illinois.edu, Advisor: Nora El-Gohary, University of Illinois at Urbana-Champaign 1. Problem Statement

4. Preliminary Results

Manual checking of construction regulatory compliance is timeconsuming, costly, and error-prone. Automating the process of compliance checking is expected to reduce time, cost and error of the process. Previous research and development efforts in automated compliance checking (ACC) have paved the way, but they have been limited because 1) they require manual understanding, extraction, and formulation of regulatory requirements, and 2) they lack the level of knowledge representation and reasoning that is needed for compliance analysis and checking.

4.1 Implementation Method

4.4 Automated Reasoning Results

Information Extraction

Information Transformation

Automated Reasoning

Implementation Method of the Main Algorithms

4.2 Information Extraction Methodology and Results

2. Research Objective Preprocessing

Feature Generation

Target Information Analysis

Tokenization Sentence Splitting Morphological Analysis

POS Tagging Phrase Structure Analysis Gazetteer Compiling Ontology-Based Semantic Analysis

Target Information Identification Extraction Sequence Resolution

Evaluation

Theoretically/empirically developing methodologies/algorithms and corresponding knowledge representations that 1) automatically extract regulatory and project information from textual regulatory documents and BIM models, respectively, 2) encode the information in a semantic format, and 3) automatically reason about the information for compliance assessment.

Information Representation

Partial Automated Compliance Checking Results of International Building Code 2006

(1) Project BIM Model

(2) Regulatory Documents

(5) IE Algorithms

(6) ITr Algorithms

(7) AR Algorithms

5.1 Scientific Contributions

Development of Information Extraction Rules Extraction Execution

3. Research Method Utilizing a theoretically-based, empirically-driven methodology that leverages knowledge and techniques in natural language processing (NLP), semantic modeling and automated reasoning (AR) to develop methodologies/algorithms and corresponding knowledge representations for automated information extraction (IE), automated information transformation (ITr), and automated compliance reasoning for regulatory information and project information in the construction domain.

5. Scientific Contributions and Practical Significance

Development of Extraction Rules for Extracting Single Information Elements (Matching Pattern Construction; Feature Selection; Semantic Mapping;) Development of Rules for Resolving Conflicts in Extraction

Proposed Information Extraction Methodology

Information Extraction Results for Chapter 19 of International Building Code 2009 Semantic Compliance Deontic Quantity Subject Quantitative Comparative Quantity Quantity Information Subject Checking Operator Unit/ Total Restriction Relation Relation Value Restriction Element Attribute Indicator Reference Precision 0.941 1.000 0.935 0.979 0.947 1.000 0.976 0.976 1.000 0.969 Recall

0.941

0.833

0.956

0.958

0.931

0.935

0.976

0.953

0.900

0.944

F-Measure 0.941

0.909

0.945

0.968

0.939

0.967

0.976

0.964

0.947

0.956

4.3 Information Transformation Results Information Transformation Results for Chapter 12 of International Building Code 2006 Performance Measures

(8) Extracting Project Information (Logic Facts)

(9) Extracting Rules (Logic Rules)

(10) Testing Compliance

(11) Reporting Compliance

Concept Logic Clause Elements

Relation Logic Clause Elements

(3) Semantic Model

(4) NLP Techniques

Automated Compliance Checking Framework

Total

Precision Recall F-Measure Precision Recall F-Measure Precision Recall F-Measure

Top-Down Method

Bottom-Up Method

0.961 0.923 0.941 0.948 0.927 0.938 0.954 0.925 0.939

0.997 0.976 0.986 0.938 0.951 0.944 0.962 0.961 0.962

• Offer efficient-to-develop, semantic rule-based NLP methods for IE and ITr that can help capture domain-specific meaning; • Combine domain knowledge and NLP knowledge to achieve deep NLP; • Pioneer work in utilizing semantically deep NLP approach in the construction domain; • Provide benchmark semantic methods/algorithms for IE and ITr in the construction domain; • Offer a new mechanism for processing and transforming complex regulatory text into logic clauses; • Pioneer work in utilizing logic programming (LP) for the automated reasoning functionality in ACC in the construction domain. 5.2 Practical Significance • Reduce the time and cost of compliance checking in the construction domain; • Improve the accuracy of compliance checking; • Support other applications of automated information processing in the construction domain.

Estimating Optimal Labor Productivity: A Two-Prong Strategy Author: Krishna Kisi; email: kkisi@unomaha.edu School: The Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln Advisor: Eddy Rojas; email: er@unl.edu

Introduction: In an attempt to evaluate the efficiency of labor-intensive construction operations, project managers compare actual with historical productivity for equivalent operations. However, this approach towards examining productivity only provides a relative benchmark for efficiency and may lead to the characterization of operations as authentically efficient when in reality such operations may be only comparably efficient. Optimal productivity, the highest sustainable productivity achievable in the field under good management and typical field conditions, can provide an absolute benchmark for gauging performance. This poster demonstrates development and implementation of a two-prong strategy for estimating optimal productivity in labor-intensive construction operations. The first prong represents a top-down approach that estimates optimal productivity by introducing system inefficiencies into the productivity frontier which is theoretical maximum productivity that would be achieved under absolutely perfect conditions in all respects. A qualitative factor model is used to estimate the impact of system inefficiencies. This topdown approach yields the upper level estimation of optimal labor productivity. The second prong is a bottom-up approach that determines optimal labor productivity by removing non-contributory work from actual productivity in a discrete event simulation. This bottom-up approach generates the lower level estimation of optimal labor productivity. An average of the upper and lower limits reveals the best estimate for optimal labor productivity.

CONCEPT DEVELOPMENT

BACKGROUND

PF (Productivity Frontier)

Productivity

∆si

∆’si

OPUL (Upper Limit of Optimal Productivity)

OP (Optimal Productivity)

∆i ∆oi

OPLL (Lower Limit of Optimal Productivity)

∆’oi

AP (Actual Productivity)

tn

Time

Where,

Δi = Total Inefficiencies Δsi = System inefficiencies Δoi = Operational inefficiencies Δ’si = Estimate of System inefficiencies Δ’oi = Estimate of Operational inefficiencies

tn+1

Figure 2: Different Produc5vity Levels at Steady State Phase

RESEARCH METHODOLOGY Figure 1: Produc5vity Dynamics (Source: Son and Rojas 2010)

FEASIBILITY TEST: PILOT STUDY Data collection and analysis were based upon video recording collected during a pilot project. The project included lighting replacement activity conducted by Common Wealth Electric Company at Omaha South High Magnet School. In this study, QFM was used to estimate system inefficiencies and DES yielded operational inefficiency estimates.

Qualitative Factor Model (QFM) Where: ∆′!" = estimate of productivity loss due to system inefficiencies. ∆′(!"!!"!! ) = estimate of the difference between productivity frontier and the lower limit of optimal productivity. n = number of parameters. m = number of productivity factors. z = work zone (classrooms, lockers, and corridor/hallways). i = system inefficiency factors in each work zone z. Si = severity score of individual productivity factor i. Pi = probability of individual productivity factor i. TSi = total severity score (sum of severity scores for all productivity factors). Wz = relative weights of each work zone.

Figure 3: Research Methodology

CONCLUSIONS

Discrete Event Simulation (DES) Model Resource Arrival

Site Mobiliza5on

Frame Cover Removal

Close Ballast Cover

New Bulb (T8) Installa5on

Old Bulb (T12) Removal

Test OK? New Bulb Replacement

True

Ballast Cover Removal

Old Ballast Removal

Frame Cover Closure

Transfer

New Ballast Installa5on

Disposal

False

Figure 4: Discrete Event Simula5on (DES) Flow-­‐Chart

PILOT STUDY RESULTS Problem Identification

Breakdown of Problem

Modeling Technique

Productivity Value

FUTURE WORK Model Estimate

Model Output

Optimal Estimate

Δsi QFM PF (20.23) Δ’si (2.15) OPUL(18.08) OP (15.92) Δoi DES AP (12.62) Δ’oi (1.14) OPLL(13.76) • Unit of all numbers are in stations per hour!! • Fluorescent bulb replacement task achieved 80.4% efficiency (AP as a percentage of OP) Δi

An accurate estimation of optimal productivity allows project managers to determine the absolute (unbiased) efficiency of their labor-intensive construction operations by comparing actual vs. optimal rather than actual vs. historical productivity. The proposed two-prong strategy for estimating optimal labor productivity was successfully applied in this pilot study. Therefore, this pilot study demonstrates that the proposed methodology for estimating optimal productivity in labor-intensive construction operation is adequate when applied to a simple electrical operation. This study contributes to the body of knowledge in construction engineering and management by introducing a twoprong strategy for estimating optimal productivity in labor-intensive construction operations.

Future research will be focused towards the applicability of this methodology, its usefulness, and reliability across all construction operations. The research will be conducted on more complex construction operations, such as those involving crews with multiple workers performing parallel and sequential work, such as Sheet metal, plumbing, and electrical works.

Improving Construction Cost Escalation Estimation Using Macroeconomic, Energy and Construction Market Variables Mohsen Shahandashti1 and Baabak Ashuri2 1Ph.D.

Candidate, School of Building Construction, Georgia Institute of Technology, Email: sshahandashti3@gatech.edu 2Associate Professor, School of Building Construction, Georgia Institute of Technology, E-mail: baabak.ashuri@coa.gatech.edu

Introduction • Recently, the accuracy of construction cost estimates has been significantly affected by fluctuations in construction costs in the United States. • Construction cost fluctuations have been larger and less predictable than was typical in the past. • Cost escalation has become a major concern in all industry sectors, infrastructure, heavy industrial, light industrial, and building.

Research Approach

Summary of Results

Identification of leading indicators of construction cost variations

Leading Indicators of Construction Cost Variations: Macroeconomy

A pool of 16 candidate (potential) explanatory variables is initially selected based on a comprehensive literature review about construction cost variations.

200.0

190.0

Construction Cost Index (CCI)

150

240.0 190.0

6590

150.0

11

20

9

15

7

10

5

5

250.0

250.0

3

0

200.0

200.0

150.0

150.0

50

90.0

6550

40.0

6530

0

Producer Price Index

6510

Crude Oil Price

Unemployment Rate

90.0

50.0

40.0

0.0

Producer Price Index

Federal Funds Rate

250.0

8000

42

100.0

100.0

200.0

200.0

7000

40

50.0

50.0

6470

150.0

150.0

6000

0.0

0.0

6450

100.0

100.0

5000

38

50.0

50.0

4000

36

0.0

0.0

3000

34

6490

Consumer Price Index 15000

25

10000

Problems Related to Cost Variations

5000

Related Issues to Owner Organizations Related Issues to Contractors Hidden price contingencies Delayed or cancelled projects Inconsistency in budgets Unsteady flow of projects

• Bid loss due to cost overestimation • Profit loss due to cost underestimation

0

Dow Jones Industrial Average 25

15

150.0

6000.0

10

100.0

4000.0

5

50.0

2000.0

0

0.0

0.0

Prime Lending Rate 0.2000

Previous work in predictive modeling can be classified into two categories:

Number of Building 20000.00 Permits

120.000

15000.00

100.000

-0.0500

Inflation Rate

60.000 40.000

0.00

20.000

The explanatory variables of construction cost variations are identified from the pool of candidate explanatory variables using empirical tests including correlation tests, unit root tests, Granger causality tests, and Johansen’s cointegration tests.

Multivariate Time Series Modeling

Statistical Methods

Based on the results of Johansen’s cointegration tests, Vector Error Correction Models with various combinations of leading indicators and CCI are created. The long-run form of VEC model can be represented by the following equation:

• Current statistical methods, such as univariate time series models, do not have explanatory capability and they are just suitable for short-term forecasting (Wong and Ng 2010; Touran and Lopez 2006; Goh and Teo 2000; Pindyck and Rubinfeld 1998). • The forecasting power of causal models depends on the identification of appropriate leading variables (Wong and Ng 2010; Williams 1994). However, the leading variables of CCI are not known. • The temporal relations of variables is ignored in these models.

Number of Housing Starts

• Data from January 2009 to December 2011 is used to assess the predictability of the proposed multivariate time series model. • Data from January 1975 to December 2008 is used for conducting statistical tests and creating models. • The VEC model including CCI and COP is more accurate than those forecasted by the previously proposed univariate models (i.e., Seasonal ARIMA and Holt-Winters exponential smoothing).

Error Measures • Mean Absolute Percentage Error (MAPE)

p 1

yt   Ai yt i  Byt  p  C   t i 1

Equivalent representation of the above equation:

yt  1 yt 1  ...   p yt  p  C  ut Ai  ( I  1  ...  i ),

Crude Oil Price

Validation

Causal methods, such as regression models and neural networks, use economic and market factors, such as Prime Lending Rate, to forecast future trends of CCI (Trost & Oberlender 2003; Williams 1994; Akintoye & Skitmore 1994; Koehn & Navvabi 1989).

Gaps in Knowledge

0

Gross Domestic Product GDP Implicit Price Deflator

Causal Methods

Statistical methods, such as time series analysis and curve fitting, forecast future trends of CCI based on past values (Ng et al. 2000; Wang & Mei 1998; Fellows 1991; Taylor & Bowen 1987) .

50

 CCI   1.2301 - 0.7266  CCI  - 0.1979 2.7593   CCI    COP  - 0.0118 1.6681  COP  0.0165 - 0.6801 COP   t    t 1    t 2 - 0.0943 - 1.0293   CCI  0.0104 - 1.0240  CCI  - 0.0267 - 0.0384 COP  0.0348 0.0277  COP     t 3    t 4 - 0.0678 1.5072   CCI   0.1189 - 1.2923  CCI  13.0903  u1   - 0.009 - 0.1340 COP  - 0.0034 0.1599  COP   0.2164   u     t 5    t 6    2 t

Money Supply

5000.00

100

Based on diagnosis tests, the multivariate time series model including Crude Oil Price (COP) and Construction Cost Index (CCI) is the best model among other combinations.

80.000

10000.00

0.0000

Average Hourly Earnings

Research Background

10000.0 8000.0

0.0500

10

Average Weekly Hours

200.0

0.1000

15

Employment level in 250.0 Cont.

20

0.1500

20

5

• Not enough bidders

Number of Housing Starts

Consumer Price Index

150

Number of Building Permits

250.0

Example of Construction Cost Variations

• • • •

25

140.0

6570

100.0

13

100

Energy Market

250.0

240.0

140.0 6610

Construction Market

for i  1,.., p  1

• Mean Square Error (MSE)

Notation: yt is the (K×1) vector of time series at period t K is the number of variables Ai (i=1,…,p-1) are (K×K) coefficient matrices of endogenous variables containing the cumulative long-run impacts

MAPE MSE

B is (K×K) coefficient matrix C is (K×1) vector of constants

VECM including CCI and COP 0.96% 10544.9

Seasonal ARIMA 1.40% 17921.6

Holts Winter-Exponential Smoothing 2.68% 86890.7

εt is (K×1) vector of error terms

B  ( I  1  ...   p ) The coefficients of these VEC models are estimated using Gaussian maximum likelihood procedure (Johansen 1995).

Research Objective

Diagnosis of models

The objective of this research is to create multivariate time series models for improving the accuracy of construction cost escalation estimation through utilizing information available from several indicators of macroeconomic condition, energy price and construction market.

• Lack of serial correlation among residuals is diagnosed using Breusch–Godfrey serial correlation Lagrange multiplier test (Breusch 1978; Godfrey 1978) • Constant variance of residuals is diagnosed using ARCH test (Engle 1982)

© 2014, Economics of the Sustainable Built Environment (ESBE) Lab

Conclusions

•

•

Two macroeconomic variables (consumer price index and producer price index), two construction market variables (number of building permits and number of housing starts), and an energy market variable (crude Oil price) are helpful for predicting construction cost variations ahead of time. The multivariate time series model including crude oil price performed the best on predicting construction cost variations ahead of time.

Construction Research Congress (CRC) 2014

A quantitative investigation of building micro-level power management through energy harvesting from occupant mobility Neda Mohammadi, Tanyel Bulbul, John E. Taylor | {neda, tanyel, jet}@vt.edu

Motivation:

Research Questions:

Future Prospects:

 Climate change mitigation strategies: min 3billion metric tones carbon pollution reduction by 2030¹  Interplay between energy efficiency strategies and energy harvesting from emerging renewable energy resources  Macro-scale power management  Power Grid - Wind Turbines - Solar Panels - Hydro-electric generators  Is micro-scale power management possible at the building level?

 Can energy harvesting from occupant mobility offset meaningful carbon emissions?  Can accumulated energy harvested from building occupants’ mobility contribute to the electrical demand, and thus offset CO₂ emissions of buildings? Male Female Steps hrs. (88.7kg)⁶ (75.4kg)⁶ Sedentary⁷ Low active Somewhat active Active Highly active

Heel Strike ~2W

2500

31

15

19.167

4,753,857.50

483,000.00

6250

118

27

151.04

38,288,600.00

2,718,750.00

8750

302

94

577.5

145,239,062.50

10,395,000.00

11250

148

55

380.63

95,226,232.50

6,851,250.00

15000

32

11

107.5

26,958,330.00

1,935,000.00

631

202

1235.8

310,466,082.50

22,245,000.00

37075

9,313,982,475.00

667,350,000.00

KJ/month

9,313,982.48

667,350.00

kWh/month

2,587.22

185.38

1.83

0.131

tons/month

Initial Findings:

 Harvesting the energy dissipated from occupants’ mobility through wearable technology

tons/month

Total CO2e:

1.956

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

Patton Hall + Occupants⁵  Floor space: 52,750 ft²  Construction: 1926-1929  South Atlantic  Floors: 4  Wearable activity tracker device

1.956

Space Heating, 1.78 Cooling, 4.98

Computers, 3.55 Office Equipment, 1.42

Accumulated CO₂ offset

CO₂e - October 2013 CO₂e (tons)

 Off the grid micro-level power management at building level

Individual CO₂ offset form occupants’ mobility

Pilot Study:

Other, 4.62

tons

 Recent advances in bio-mechanical energy harvesting:  Susp.-load backpack² ~15 watts  Knee-mounted biomechanical energy harvester³ ~5 watts  Piezoelectric energy scavenger shoes⁴ ~2 watts

Knee Bend ~5W

Refrigeration, 1.78

3.3%

Cooking, 0.04

Lighting, 13.86

Ventilation, 3.2 Water Heating, 0.36

CO₂ [m³ ton]⁹ Occupants Patton Hall

REFERENCES:

Potential disaggregated CO₂ emission offsets due to occupants’ mobility

¹ The President’s Climate Action Plan (2013). < http://www.whitehouse.gov/sites/default/files/image/president27sclimateactionplan.pdf > (accessed on April, 2014) ² Rome, L. C., Flynn, L., Goldman, E. M., & Yoo, T. D. (2005). Generating electricity while walking with loads. Science, 309(5741), 1725-1728. ³ Donelan, J. M., Li, Q., Naing, V., Hoffer, J. A., Weber, D. J., & Kuo, A. D. (2008). Biomechanical energy harvesting: generating electricity during walking with minimal user effort. Science, 319(5864), 807-810. ⁴ Krupenkin, T., & Taylor, J. A. (2011). Reverse electrowetting as a new approach to high-power energy harvesting. Nature communications, 2, 448. ⁵ COLLEGE OF ENGINEERING DATA BOOK 2012-2013, Virginia Tech (accessed on December 2013) ⁶ Anthropometric Reference Data for Children and Adults: United States 2007-2010 .< http://www.cdc.gov/> (accessed December 2013) ⁷ Tudor-Locke, C., Johnson, W. D., & Katzmarzyk, P. T. (2009). Accelerometer-determined steps per day in US adults. Medicine and science in sports and exercise, 41(7), 1384-1391. ⁸ Electricity Consumption (kWh) by End Use for Non-Mall Buildings, 2003, < www.eia.gov/ > (accessed December 2013)

 Energy harvesting, conversion and exchange in a building-occupant system from occupants’ locomotion: as an alternative micro-scale energy system can be employed in buildings in support of off the grid micro-level power management.  Public Infrastructure with high human mobility: in aggregate could offset a meaningful portion of building CO₂ emissions  Harvesting energy generated by the human mobility of building occupants would take advantage of a vast and untapped renewable energy resource.

Ubiquitous adoption  accumulated energy in large scale

Vahid Balali

Segmentation and Recognition of Roadway Assets from Car-Mounted Camera Video Streams Using a Scalable Non-Parametric Image Parsing Method

(c)

(b)

(d)



Given a query (test) image Retrieval set of 200 similar images by taking the minimum per-feature rank of four global features



For each test superpixel Xi described by multiple local feature đ?‘“đ?‘–đ?‘˜ , compute a likelihood ratio score for each class a found in the retrieval set đ?‘ƒ(đ?‘‹đ?‘– |đ?‘Ž) L đ?‘‹đ?‘– , đ?‘Ž = đ?‘™đ?‘œđ?‘” = đ?‘™đ?‘œđ?‘” đ?‘ƒ(đ?‘‹đ?‘– |đ?‘Ž)

Create and validate an automated roadway asset data collection and assessment tool  Data Collection, analysis, and representation  A new scalable method for segmentation and recognition of roadway assets from videos  Reduce the time and effort for road inventories especially for guardrails and light poles

 

Semantic Labels

Asset Labels

Geometric Labels

Graph-based Segmented Superpixels

Guardrail

Tree

Pavemen t

Shoulder

Pole

Traffic Sign

Query Video Frame

(d)

Data

5. Data Collection and Setup 

Smart Road Dataset



Interstate I-57 Dataset

450 400 350 300 250 200 150 100 50 0

Semantic Labels

Traffic Sign

Guardrail Pavement Marking Asphalt Pavement

Vertical

Sky

Horizontal

Testing Image Geometric

Ground Truth Geometric Labels

Asphalt Pavement

Concrete Pavement

Guardrail

Horizontal

Vertical

Sky

Pole

Ground Truth Semantic Labels

Label Superpixels

Extract Superpixel Features

Query Frame Superpixels

Tree

Ground Truth Segmentation

2500 2000 1500 1000 500 0

Segmented Video Frames

Pavement Marking

Average accuracy of 78.67 % Smart Road Dataset

Testing Image Semantic

Traffic Signal

0 1

Likelihood Ratio Scores per Class for Each Superpixel

Bank of Superpixel Filters

Process

Video Frames

Geometric Labels 800 700 600 500 400 300 200 100 0

Query

đ??¸đ?‘ đ?‘šđ?‘œđ?‘œđ?‘Ąâ„Ž (đ?‘Žđ?‘– , đ?‘Žđ?‘— ) đ?‘‹đ?‘– ,đ?‘‹đ?‘– ∈đ?œ“

đ?œ =

Retrieval Set of Training Images

6. Experimental Results

2500 2250 2000 1750 1500 1250 1000 750 500 250 0

Bridge

Bank of Global Filters Detect Global Features

Pavement Marking

(b)

đ?œ (đ?‘Žđ?‘– , đ?‘”đ?‘– ) đ?‘‹đ?‘– ∈đ?œ“

(e)

(c)

đ?‘˜

đ?‘ƒ(đ?‘“đ?‘–đ?‘˜ |đ?‘Ž) đ?‘™đ?‘œđ?‘” đ?‘ƒ(đ?‘“đ?‘–đ?‘˜ |đ?‘Ž)

Simultaneously solve for a ďŹ eld of semantic and geometric labels over the image by optimizing

Training Video Frames

(a)

đ??¸đ?‘‘đ?‘Žđ?‘Ąđ?‘Ž đ?‘‹đ?‘– , đ?‘Žđ?‘– + đ?œ† đ?‘‹đ?‘– ∈đ?œ‘

đ??ť đ?‘Ž, đ?‘” = đ??˝ đ?‘Ž + đ??˝ đ?‘” + đ?œ‡

Retrieval Image Set

đ?‘˜

đ?‘ƒ(đ?‘“đ?‘–đ?‘˜ |đ?‘Ž) = đ?‘ƒ(đ?‘“đ?‘–đ?‘˜ |đ?‘Ž)

Use MRF inference to solve for the label field a = đ?‘Žđ?‘– over the entire test image đ??˝ đ?‘Ž =

3. Graphical Overview Query Video Frame



đ?‘–đ?‘“ đ?‘Ąâ„Žđ?‘’ đ?‘Žđ?‘– đ?‘–đ?‘ đ?‘œđ?‘“ đ?‘Ąâ„Žđ?‘’ đ?‘Ąđ?‘Śđ?‘?đ?‘’ (đ?‘”đ?‘– đ?‘‚đ?‘Ąâ„Žđ?‘’đ?‘&#x;đ?‘¤đ?‘–đ?‘ đ?‘’

Markov Random Field

Semantic Labeling Geometric Labeling

Asset Label

Average accuracy of 82.02 % Interstate I-57 Dataset

Soil

7. Contributions and Significance  

Video-based parsing and segmentation

   

Leveraging motion cues & temporal consistency

Improving the performance of 3D assets recognition (e.g. guardrails, light poles)

Reducing time and effort required for inventory An average accuracy of 88.24% for recognition An average accuracy of 82.02% for segmentation

Real-Time and Automated Monitoring and Control (RAAMAC) Lab

4. Method

1. Motivation  Manual data collection, time-consuming, subjective (a)  Significant expansion in size and complexity  Lack of techniques to easily track, analyze and visualize 2. Research Objective and Highlights

University of Illinois at Urbana-Champaign Advisor: Mani Golparvar-Fard

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