EAR Program—video analytics research 

Exploratory Advanced 

Research Program 

Video Analytics 

Research Projects
b  |  EAR Program—video analytics res

Notice

This document is disseminated under the sponsorship of the U.S. Department 

of Transportation in the interest of information exchange. The U.S. Government 

assumes no liability for the use of the information contained in this document. 

This report does not constitute a standard, specification, or regulation.

The U.S. Government does not endorse products or manufacturers. Trademarks 

or manufacturers’ names appear in this document only because they are 

considered essential to the objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information 

to serve Government, industry, and the public in a manner that promotes public 

understanding. Standards and policies are used to ensure and maximize the 

quality, objectivity, utility, and integrity of its information. FHWA periodically 

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continuous quality improvement.

Cover photos show, left, hands on steering wheel segmentation using skin color, from Carnegie 

Mellon University’s research into an automated real-time system to analyze emotional states of 

the driver (© Carnegie Mellon University); and, right, an overview of the system that will extract 

driver behaviour features and recognize various actions from SRI International’s research into a 

comprehensive automatic coding system (© SRI International).
EAR Program—video analytics research 

Introduction 2

Data Bottleneck 2

Funding Research 2

Research Goals 3

Initial Research 4

Machine Learning for Automated Analysis of Large Volumes 

of Highway Video 4

Automated Feature Extraction 6

DCode: A Comprehensive Automatic Coding System for 

Driver Behavior Analysis 6

DB-SAM: CMU Driver Behavioral Situational Awareness System 7

Quantifying Driver Distraction and Engagement Using Video Analytics 8

Automated Identity Masking 10

Automation of Video Feature Extraction for Road 

Safety-Automated Identity Masking 10

DMask: A Reliable Identity Masking System for Driver Safety Video Data 11

Benchmarking Research Progress 12

Getting Involved with the EAR Program inside back cover

Learn More inside back cover

EAR Program Results back cover

Contents
Transportation researchers are starting to use extremely large datasets to identify and understand 

complex transportation issues that can impact transportation efficiency, cost, and safety. New 

automated tools for data extraction and analysis will help make these massive datasets accessible to the widest possible range of researchers, dramatically reducing both the cost and the time it 

takes to process the raw data. Developing new techniques to automatically process and analyze such 

large quantities of data is the goal of several recent research projects funded by the Federal Highway 

Administration’s (FHWA’s) Exploratory Advanced Research (EAR) Program.

The Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) study 

demonstrates the immense scale of data being gathered. The flagship dataset to emerge from the 

SHRP 2 study is the naturalistic driving study (NDS). This represents the largest ever study designed 

to better understand the interaction among various factors involved in highway crashes—driver, 

vehicle, and infrastructure—so that better safety countermeasures can be developed and applied to 

save lives. The study is a focus on driver behavior and addresses the notion that it is possible to obtain 

more and better information on what people do when they drive—not just in the moments before they 

get into a collision but on a day-to-day basis. NDS helps deliver the solid baseline reference that is 

required to provide context to assess what is high-risk behavior and what is considered normal. 

Data Bottleneck

Researchers for the NDS have gathered over 1.2-million hours of data, collected from the vehicles of 

approximately 3,000 volunteers going about their regular activities. Each of those vehicles was equipped 

with four cameras, a Global Positioning System (GPS), and many other sensors. Over 2 petabytes 

(2,000 terabytes) of data has been generated over a 2-year period, a majority of which comes from 

video captured by the onboard cameras. The massive size of the video data creates a serious data 

bottleneck for researchers and makes traditional methods for identifying features in the data, such 

as objects, behaviors, roadside design details, and surrounding vehicles, completely inadequate. The 

traditional way to extract features of interest from video is for a researcher to physically sit in front of 

a monitor and manually log the location in the video data where each feature of interest is found; 

however, it is estimated that it would take almost 600 technicians a full year, working 40 hours per 

week, to go through all the video in the NDS. 

Funding Research

Several video analytics research projects, funded by the EAR Program, aim to make data processing 

and analysis more practical by focusing on increasing the automation of video data decoding. The goal 

of these EAR Program projects is to assist researchers through the use of an algorithm designed to 

narrow down the amount of data researchers need to manually review. This will enable researchers to 

quickly and accurately extract the required details from large data files.

There are six ongoing video analytics projects funded by the EAR Program, all tasked with developing 

technology to make it feasible for a researcher to quickly and flexibly get what they need from an 

extremely large dataset. The first video analytics research project was awarded to Carnegie Mellon 

University’s National Robotics Engineering Center to investigate machine learning to teach an algorithm 

how to find and classify features of interest in the forward facing video data. 

Introduction

2  |  EAR Program—video analytics res
Learnings from this first award were examined and used to subsequently award Carnegie Mellon 

University, SRI International, and University of Wisconsin–Madison, three additional research projects 

to investigate how to automate the identification and classification of driver behavior, but focused on 

events inside the vehicle. 

FHWA awarded two further EAR Program projects to Carnegie Mellon University and SRI International 

to focus on automated driver identity masking. The NDS dataset includes personally identifiable information related to the drivers, including video looking right at their faces and GPS locations of each trip 

taken. Protection of the privacy of the volunteers means that if this sensitive personal information is 

to be made available to the researcher, the researcher will have to travel to a secure enclave, which 

is equipped to prevent any unauthorized viewing or recording of the volunteers’ faces. These two 

research teams are working on algorithms that can automatically find faces in the video data and 

obscure the actual face in a non-reversible way, while still retaining certain points on the face that 

allow the researcher to identify and track expression. If this effort is successful, many researchers will 

be freed from the significant burden that use of the sensitive personal information would entail.

Research Goals

The Government’s goals are both short and long term. In the short term, the Government wants 

to extract value from the NDS data. In the long term, the Government wants to ensure that the data 

being collected will improve transportation safety to the maximum possible extent. The EAR Program 

addresses the need for longer term, higher risk research with the potential for long-term improvements to transportation systems—improvements in planning, building, renewing, and operating 

safe, congestion-free, and environmentally sound transportation facilities. The EAR Program seeks to 

leverage advances in science and engineering that could lead to breakthroughs for critical current and 

emerging issues in highway transportation—where there is a community of experts from different 

disciplines who likely have the talent and interest in researching solutions and who likely would not do 

so without EAR Program funding. 

The following pages contain summary descriptions of the EAR Program-sponsored research projects 

investigating video analytics.

EAR Program—video analytics research 
4  |  EAR Program—video analytics res

Initial Research

PROJECT: Machine Learning for Automated Analysis of Large Volumes of Highway Video

INSTITUTION: Carnegie Mellon University

PERIOD: October 2012–September 2015

OBJECTIVE: To develop a prototype tool that enables machine-learning algorithms to extract useful roadway 

features from video. 

contact: Lincoln Cobb, Office of Safety Research and Devel

SUMMARY: The SHRP 2 NDS is an unprecedented, extremely large

dataset which includes over 1-million hours of video collected 

from vehicles traveling on U.S. highways. The NDS is being used 

for a wide range of research activities. However, as stated in 

the previous pages, such massive video datasets make data 

management and access a time-consuming and expensive challenge. The objective of this research project is to develop a highly 

usable prototype software tool that integrates and develops 

computer vision and machine-learning algorithms to automatically extract useful features from forward-looking video cameras 

mounted on volunteers’ vehicles. 

A machine-learning-based approach enables researchers to 

harness the power of massive, diverse, and complex datasets to 

improve the performance of the feature extraction algorithms, 

instead of being overwhelmed by the quantity of data. Such a 

tool will make it feasible for a researcher to efficiently extract 

exactly what they need from a large video dataset. Though the 

primary application of the results of this work is the SHRP 2 NDS, 

the research team focused much of their work on the Roadway 

Information Database (RID), the second element of the SHRP 2 

Safety program. The RID includes forward video collected 

by specialized data collection vehicles on some of the roads 

travelled by the NDS volunteers. That video information, and 

other data collected by the video van, is analyzed to generate a 

great deal of geometric and asset inventory data, including 

curve dimensions, grade, and cross slope. In addition, the RID 

includes information provided by local transportation agencies, 

such as the location and duration of work zones and weather 

information. The research team also worked with the SHRP 2 

NDS forward-looking video streams; however, the lower-resolution 

NDS dataset represents significant challenges when attempting 

to identify certain features within a scene. 

The researchers for this project integrate a range of previously 

developed analysis techniques with traditional object detection 

methods. They combined several state-of-the-art machine-learning 

algorithms to address a range of detection requirements; for 

example, a segmentation algorithm was used to help understand 

the context of a scene and a separate vehicle detector algorithm 

was used to detect cars and trucks. Learning algorithms can be 

trained on very large datasets and are capable of learning with 

high accuracy exactly which features are important, which 

provide less useful data, and how features can be used in combination to accurately recognize and classify the desired targets. 

This means that the great diversity provided by large and complex 

datasets actually adds to the power and robust performance of 

the system. The researchers developed a simple graphical user 

interface (GUI) to wrap up all of the individual analysis tools 

and enable even a novice user to quickly label data, train the 

detectors, and evaluate the performance of newly trained or 

existing detectors. 

The research team evaluated multiple solutions to confront a 

range of challenges, including how to handle overlapping objects, 

as shown in figure 1; identify different roadway features (e.g., car 

and truck detection); understand a scene and distinguish between 

vegetation, sky, and highway shoulder; detect traffic signs; and 

detect and estimate the status of traffic lights. Efforts to overcome these challenges led to the development and refinement of 

several innovative components, including the development of a 

new traffic light detection and signal estimation system focused 

on NDS data, and a new configurable traffic sign detection 

system. Moving forward, the research team has proposed a hybrid 

approach to supplement the NDS data with higher-quality data, 

such as from the RID. In accordance, the research team recommended using NDS data to detect dynamic information, such as 

traffic light state, vehicles, weather, construction zones, and 

vehicle–lane positioning, and then using higher-quality datasets 

from the same location to detect static information, such as 

roadway characteristics, signs, and traffic light location. 

The first video analytics research contract was awarded by the EAR Program to Carnegie Mellon University in October 

2012. The following summary provides an overview of this research activity.
EAR Program—video analytics research 

Figure 1. A typical challenging situation for overlapping objects (left vehicles) and occluded objects (right 

vehicle) in an urban environment.

This research project is directed at the development of tools 

to automate the initial coding of this video data—that is, the 

identification and classification of information of interest to safety 

researchers. This toolset relies on advanced machine-learning 

principles, including a strong reliance on context, to allow the 

analyst to find the features needed to answer particular research 

questions. It is based on the concept that the overall video data 

are not as useful as understanding the actual scene content. 

For example, if a researcher is interested in driver behavior at 

an intersection, then all the non-intersection video content is 

irrelevant. Identifying images of interest based on the task at 

hand, and automatically identifying the scene content, has the 

benefit of significantly reducing costs and enables researchers 

to do more with the data. Automating the identification of 

interesting images and automatically extracting the desired 

scene content is therefore a crucial component that is necessary 

to fully leverage the power that such large datasets have to offer 

transportation researchers. 

For more information on this project, visit www.fhwa.dot.

gov/research/tfhrc/projects/projectsdb/projectdetails.cfm?

projectid=FHWA-PROJ-12-0070.

Impact: This research will provide an improved understanding

how computer vision techniques could be better used to support 

transportation research. The prototype system demonstrates the 

effectiveness of advanced machine-learning techniques applied 

to large, diverse datasets. This research has a particular focus on 

techniques which may compensate for poor quality data with 

poor resolution, and lays the groundwork for future development 

of a comprehensive library of data processing and analysis 

tools. Fully understanding these data will enable researchers to 

establish meaningful answers to a broad range of challenging 

questions, without the need to perform expensive data collection. 

This research is expected to open further avenues for active 

learning for traffic and congestion control measures, driver 

behavior training, and road safety mechanisms. © Carnegie Mellon University
6  |  EAR Program—video analytics res

Automated Feature Extraction

PROJECT: DCode: A Comprehensive Automatic Coding System for Driver Behavior Analysis

INSTITUTION: SRI International

PERIOD: March 2014–March 2016

OBJECTIVE: To develop a comprehensive automatic coding system to assist in the coding of features in 

the SHRP 2 NDS data relevant to safety researchers. 

contact: Lincoln Cobb, Office of Safety Research and Devel

SUMMARY: Understanding the interaction among various factors

involved in highway crashes is one of the key goals of the SHRP 2 

NDS. Improved understanding in this area will ultimately permit 

the development of better safety countermeasures. The volume 

of data gathered in this study creates a need for a system that can 

automatically annotate the data in an accurate and expedient manner. SRI International 

is developing a comprehensive automatic 

coding system, known as DCode, to assist 

in the coding of features in the video data 

relevant to safety researchers interested in 

using the SHRP 2 NDS data. 

The system will extract driver behavior 

features, including head pose, gaze direction, 

eye blinks, mouth movements, facial expressions, upper-body and hand positions, as 

well as recognize various actions (e.g., 

using a cell phone) and gestures made by 

the driver, as shown in figure 2. The system 

will also track features from the driving 

environment, including passengers in the 

vehicle, pedestrians, vehicles, and traffic 

and weather conditions outside the vehicle. 

According to the research team, a 

comprehensive driving behavior study must 

© SRI International

I

n 2014, the EAR Program sponsored three additional research awards. The awards were made to SRI International, Carnegie 

Mellon University, and the University of Wisconsin–Madison. These projects reflected lessons learned from the initial research 

project conducted by Carnegie Mellon University, “Machine Learning for Automated Analysis of Large Volumes of Highway 

Video.” The objective of these new research projects is to advance efficient and cost-effective methods and tools to analyze 

the large amounts of video-related safety data generated by studies such as the SHRP 2 NDS, but focused particularly on the 

driver and the interior of vehicles involved. Each project brings different technical strengths and approaches, matching 

computer science with highway engineering and applying the approaches to different features of interest for highway safety. 

Such tools will reduce the time and costs required to pull needed features from these rich datasets, and thus expand the pool 

of researchers able and willing to use data, such as the NDS, in their research. In time, as the safety community’s understanding 

of the relationships between driver behavior and safety increases, these EAR Program-sponsored research projects could 

enable accelerated development of safety improvements in many areas of transportation.

The following pages provide a brief summary of each of these projects.

take into account not only the actions and behaviors of the driver 

but also the context in which those actions are performed. The 

term context refers here to factors outside the vehicle, such as 

weather, road, and traffic conditions, in addition to actions and 

signals from nearby vehicles, traffic lights, and road signs. It also 

Figure 2. The DCode System will extract driver behavior features and recognize various actions. It will also track 

contextual features.
EAR Program—video analytics research 

PROJECT: DB-SAM: CMU Driver Behavioral Situational Awareness System

INSTITUTION: Carnegie Mellon University

PERIOD: March 2014–March 2016

OBJECTIVE: To develop an automated real-time system to analyze emotional states of the driver to determine 

if he or she is fatigued or distracted. 

CONTACT: Craig Thor, Office of Safety Research and Developmen

SUMMARY: Driver safety is a major concern in today’s world and

the ability to use image processing, computer vision, and machinelearning algorithms to automatically assess whether a driver is in 

good condition to drive is of extreme importance. The research 

team for this project will develop an automated real-time system 

to analyze the emotional state of the driver to determine if he or 

she is tired or starting to fall asleep, distracted by a conversation, 

or using a handheld device. The system will also continuously 

estimate the head pose of a driver to see if they are paying attention 

to the road and surroundings. The research team’s previous 

experience in detecting and tracking faces in a crowd in complex 

environments is highly relevant to this project. This is because a 

similar challenging scenario arises when a person drives through 

various scenes with difficult lighting conditions and visual noise. 

For this project, the research team plans to delineate regions 

of interest on the face that need to be analyzed. To realize the goal 

of a fully automated real-time driver safety analysis system, the 

research team is using a landmarking tool, known as a modified 

active shape model, to quickly and reliably provide accurate 

landmarks irrespective of pose variations and partial occlusions 

of the face. The research team is also using a fast and robust 

three-dimensional (3-D)-based reconstruction technique that 

uses a single view instead of multiple views, known as a 3-D generic 

elastic model. Texture-based classifiers will be built to determine 

if a driver’s mouth is open or closed and subsequently used to 

indicate if the driver is talking. Likewise, the eyes will be tracked 

to determine if they are open, closed, or partly closed, and used 

to indicate driver fatigue (figure 3). 

Instead of pursuing methods to determine the emotional state 

of a driver based on facial expressions, this research is focused 

on the fundamental question of whether a driver is in a condition 

to safely drive the vehicle. The research team claims this question 

can be best answered by simply monitoring the eyes and lips for 

signs of fatigue or distraction. Segmentation-based algorithms 

will also be used to check if a driver’s hands are on the steering 

wheel, as shown in figure 4, and compressed sensing-based 

algorithms will be used to determine whether a driver is talking 

on a cellphone held next to their ear. To detect if a driver is 

wearing a seatbelt, a specific region in the image will be located 

Figure 3. Examples of how the distance between the curves of the eyelids 

could be used for eye openness classification.

© Carnegie Mellon University

refers to factors inside the vehicle, such as passengers and any 

distractions they may cause, vehicular alarms, satellite navigation 

and other gadget-induced distractions, and various other objects 

the driver may interact with. The system under development uses 

existing software and algorithms to track features on the driver’s 

body, including the head, face, and hands, as well as features from 

outside the vehicle that are relevant to safety research, such as 

pedestrians, vehicles, vehicle signals, and weather conditions. 

The research team aims to (1) retrain current algorithms with 

data that better matches the SHRP 2 NDS data and also explore a 

suite of novel feature extraction and tracking approaches better 

suited to this data; (2) extract features related to the driver’s 

behavior, as well as to the driver’s environment inside and outside 

the vehicle, to provide valuable contextual features; (3) develop a 

multitiered feature extraction pipeline where the core layer will 

track all directly observable features, such as the head pose, facial 

features, upper-body and hand positions, and pedestrian and 

vehicle locations—while the upper layers will use these features to 

identify various actions and gestures as well as monitor the driver’s 

state based on machine-learning techniques; and (4) develop 

algorithms in a manner that makes them scalable so that it may 

be run on a distributed computational architecture. Application 

programming interfaces and data interfaces for feature extraction 

will also be developed by the research team that are amenable to 

remote Web-based access and processing of large volumes of 

data without any humans in the loop.
8  |  EAR Program—video analytics res

Automated Feature Extraction (continued)

PROJECT: Quantifying Driver Distraction and Engagement Using Video Analytics

iNSTITUTION: University of Wisconsin–Madison

PERIOD: April 2014–August 2015

OBJECTIVE: To develop an open-software platform and GUI that will enable automated feature extraction and behavior 

characterization and visualization of naturalistic driving video, specifically SHRP 2 data. 

CONTACTS: David Yang, Office of Safety Research and Developmen

 Michelle Arnold, Office of Safety Research and Development

SUMMARY: Driver distraction accounts for a large proportion of

crashes. In fact, an estimated 421,000 people were injured in 

motor vehicle crashes involving a distracted driver in 2012.(!) Video 

data is critical to understanding driver behavior while driving 

but manual coding of video data to allow such analysis is at a 

critical bottleneck. This research project investigates automated 

or semi-automated video coding as a potentially transformative 

technology for both naturalistic and simulator studies. The goal 

is to develop an extendable, open-source platform and GUI that 

will enable automated feature extraction, behavior characterization, 

and visualization of SHRP 2 multimodal data.

The research approach is three-phased and starts with the 

application of image enhancement techniques to improve data 

quality before actual feature extraction. Known as pre-processing, 

this process prepares the video to be analyzed by the feature 

extraction software. Next, automated feature extraction will be 

performed in batches. Here, an algorithm is used to automatically 

segment each video frame into regions of interest (ROI) so that 

the contextual relevant regions may be isolated for further analysis. 

Examples of ROIs include the driver’s head and face, upper body, 

steering wheel, roadway, and objects on roadways, as shown in 

figure 5. The software can then detect and estimate head and 

body pose using facial landmarks (e.g., ear, nose, eyes, and mouth). 

These landmarks are prominent, well-defined facial features that 

can be easily tracked between successive frames. Using these 

measurements, the research team can reliably assess a driver’s 

safety as accurately as using manual annotation of video footage. 

The software also monitors hand activity to determine if a 

driver’s hand is on the steering wheel. To achieve this, an ROI can 

be defined that matches the area of a steering wheel and then 

1

 Distraction.gov. (2015). Key facts and statistics. Retrieved 28 January 2015, from 

http://www.distraction.gov/content/get-the-facts/facts-and-statistics.html

Figure 4. Hands on steering wheel segmentation using skin color.

© Carnegie Mellon University

for the presence of seat belts. The image can be automatically 

processed and a linear regression-based model applied to detect 

the presence of a seatbelt. In addition, the ability to detect soft 

biometric information could eventually be used to improve 

and tailor behavioral advice; for example, identifying whether a 

driver is over 18 years, or if a driver is wearing sunglasses when 

driving at night.
EAR Program—video analytics research 

Figure 5. Regions of interest, including the driver’s face, hands, and front view, are monitored by the system.

© Regents of the University of Wisconsin–Madison

used as a binary image to determine if the hand is on the wheel or 

not. In addition, the software is able to detect eye and mouth 

movement and then use a driver’s gaze pattern to reveal their 

state of mind during driving; however, this is not a straightforward 

process because staring straight ahead may not necessarily 

imply engagement in driving, just as looking right and left over 

the shoulder could be a sign of alertness rather than distraction. 

To truly capture a driver’s gaze status, the research team is 

focusing on whether the eyes are closed for a prolonged period 

of time and observing mouth movement to focus on conversations 

that last more than 5–10 seconds. Talking may also involve head 

movements and hands off the wheel, which can also be measured 

as further evidence of driver distraction. The ability to detect the 

location of the vehicle offers valuable contextual information to 

infer a driver’s behavior. Including information on driving speed 

and GPS data would enable researchers to determine whether a 

vehicle is stopped at a traffic light or a stop sign and see if a driver 

swings the head to look left and right when the vehicle stops. 

The final phase of this research includes a quality assessment 

to validate the accuracy of the extracted features as extraction 

proceeds. Inconsistencies will be annotated and made available for 

human operator review. The software will include a fully automatic 

feature-coding mode and an interactive, human-assisted mode. 

The interactive mode will consist of visualization of the extracted 

features, and a GUI that will access a database consisting of the raw 

SHRP 2 data and extracted features. Users will be able to review the 

raw SHRP 2 composite video, together with a graphic animation of 

the extracted features, as well as a display of the multimodal driver 

states, environmental variables, and driving conditions.

FACE HANDS FRONT

Video Player Controls DISTRACTION

Video Set: 5 

10

Fully 

Distracted

8

Generally 

Distracted

6

Substantially 

Distracted

4

Minimally 

Distracted

2

Almost Not 

Distracted

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Not 

Distracted

Load

Stop

PAUSE

GO TO NEXT VIDEO
10  |  EAR Program—video analytics res

Automated Identity Masking

PROJECT: Automation of Video Feature Extraction for Road Safety-Automated Identity Masking

iNSTITUTION: Carnegie Mellon University and University of Pittsburgh

PERIOD: February 2014–April 2015

OBJECTIVE: To develop an automated facial masking technique to de-identify face images.

CONTACT: James Pol, Office of Safety Research and Developmen

SUMMARY: The researchers for this project are developing an

automated facial-masking technique to de-identify face images 

while preserving the facial behaviors of the drivers. The facial 

de-identification process will be non-reversible so that the driver 

identity cannot be re-established once masked. At the core of 

the research is a new concept referred to as facial action transfer 

(FAT), which clones the facial actions from the video of one 

person to another person, as shown in figure 6.

FAT represents several innovations when compared to traditional image distortion methods. The concept replaces person–

specific facial features (i.e., identity information) of the subject to 

be protected with those of the target. It also preserves facial 

actions by generating video-realistic facial shape and appearance 

changes on the target face. The photo-realistic and video-realistic 

de-identified video preserves spontaneous and subtle facial 

movements, while de-identifying the identity of the driver. 

The software uses only one picture of the mask face to 

produce realistic videos where the facial appearances of the 

driver are replaced by the mask face. The research team has 

already built a fully automatic GUI to track the facial features 

in the video and detect facial landmarks in the mask face. The 

GUI is then able to output videos in which the driver face is 

de-identified. The research team is now exploring algorithms 

designed to smooth the de-identified facial actions over time to 

prevent issues occurring when there are sudden changes of 

ambient light or movement of the driver’s head.

The EAR Program supported two additional research awards, one each to Carnegie Mellon University and to SRI International. 

These two institutions were charged with investigating automated identity masking and both are working on individual 

approaches to ultimately make it easier for a wide range of researchers to preserve privacy issues while making use of the NDS 

and related datasets. Development of facial masking that provides underlying information about the driver (e.g., head pose, 

mouth, and eye movement) while precluding personal identification, would allow for increased access and accelerated conduct 

of research using naturalistic driving data.

The following pages provide a brief summary of these two projects.

Figure 6. The personalized facial action transfer process.

Chart: © Carnegie Mellon. Face images: © University of Pittsburgh.
EAR Program—video analytics research p

Figure 7. A driver’s identify is protected using an avatar to replace their face in all frames.

PROJECT: DMask: A Reliable Identity Masking System for Driver Safety Video Data

iNSTITUTION: SRI International 

PERIOD: February 2014–February 2016

OBJECTIVE: To develop an automated, complete, and irreversible identity masking system for processing realistic, 

low-resolution video in the SHRP 2 dataset.

CONTACT: Aladdin Barkawi, Office of Safety Research and Developmen

SUMMARY: The research team for this project is using innovative

technologies to replace the driver’s head in the full SHRP 2 

dataset with the head of a computer-generated avatar. The 

system will preserve information about the driver’s head pose, 

facial expressions, the state of their eyes and mouth, and the 

direction of their eye gaze.

This project uses a four-layered processing framework to detect 

and track the driver’s head and facial features, interpolate the 

driver’s head position in frames that the tracker misses, replace 

the head with a rendered avatar in all frames to mask the driver’s 

identity, and evaluate the confidence of this identity masking 

(figure 7).

In addition, the research team will develop an approach to 

synthesize facial motions on the computer generated avatar that 

closely tracks the driver’s facial motions. This enables researchers 

working with the identity-masked videos to still have access to 

behaviorally relevant cues on the driver’s face. The research 

team will also develop a method to detect (and not mask) 

non-facial elements obscuring the face (e.g., the driver’s hand), 

so that information loss is minimized in the videos. 

The final step in the process is the development of a GUI tool 

that displays the face-masked version along with a measure of 

confidence of masking over the entire video. This will allow a 

human user to quickly view the lowest confidence frames in the 

masked video and, with very little effort, re-annotate some 

key facial points manually in frames where the tracking and or 

masking was incorrect, thus allowing the system to recover from 

those mistakes.

© SRI International
12  |  EAR Program—video analytics res

INSTITUTION: Oak Ridge National Laboratory 

PERIOD: June 2013–May 2017

OBJECTIVE: To develop calibration and measurement techniques that will aid the broader community of researchers that want to 

work with NDS data.

CONTACT: Lincoln Cobb, Office of Safety Research and Developmen

SUMMARY: FHWA is working with the Oak Ridge National

Laboratory to develop calibration and measurement techniques 

that will enable benchmarking of research progress and technical 

assessment of EAR Program-sponsored research teams. Tasks 

include creating camera calibration models, testing baseline 

algorithms for proposed feature extraction and identity masking, 

creating partitions of data into training and validation sets, 

developing evaluation method for automated identify masking, 

and conducting computational load projections and data 

sharing architectures.

Benchmarking Research Progress

FHWA is also working with the Department of Energy’s Oak Ridge National Laboratory to develop tools for benchmarking 

research progress. A brief summary of this collaboration is included below.
EAR Program—video analytics research 

Getting Involved with the EAR Program

To take advantage of a broad variety of scientific and engineering discoveries, the EAR 

Program involves both traditional stakeholders (State department of transportation 

researchers, University Transportation Center researchers, and Transportation Research 

Board committee and panel members) and nontraditional stakeholders (investigators 

from private industry, related disciplines in academia, and research programs in other 

countries) throughout the research process.

Learn More

For more information, see the EAR Program Web site at www.fhwa.dot.gov/advanced

research/cvtr.cfm.
d  |  EAR Program—video analytics res

FHWA-HRT-15-025

HRTM-30/5-15(1M)E

EAR Program Results

The EAR Program strives to develop partnerships with the public and private 

sectors because the very nature of exploratory advanced research is to apply 

ideas across traditional fields of research and stimulate new approaches to 

problem solving. The program bridges basic research (e.g., academic work funded 

by National Science Foundation grants) and applied research (e.g., studies 

funded by State departments of transportation). In addition to sponsoring 

exploratory advanced research projects that advance the development of highway infrastructure and operations, the EAR Program is committed to promoting 

cross-fertilization with other technical fields, furthering promising lines of 

research through dissemination and continued investigations, and deepening 

vital research capacity.