ARL-TR-8255 ● DEC 2017

US Army Research Laboratory

Advanced Video Activity Analytics (AVAA): 

Human Performance Model Report

by Beth M Plott, Patricia L McDermott, and Michael Barnes

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ARL-TR-8255 ● DEC 2017

US Army Research Laboratory

Advanced Video Activity Analytics (AVAA): 

Human Performance Model Report

by Beth M Plott and Patricia L McDermott

Alion Science and Technology, Boulder, CO

Michael Barnes

Human Research and Engineering Directorate, ARL

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1. REPORT DATE (DD-MM-YYYY)

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April 2015 – June 2016

4. TITLE AND SUBTITLE

Advanced Video Activity Analytics (AVAA): Human Performance Model 

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Beth M Plott, Patricia L McDermott, and Michael Barnes

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13. SUPPLEMENTARY NOTES

14. ABSTRACT

This report describes the initial human performance modeling effort of the Advanced Video Activity Analytics (AVAA) 

system. AVAA was designed to help US Army Intelligence Analysts exploit full-motion video more efficiently and 

effectively. The goal of the modeling effort is to provide an understanding of the current state of the system with respect to the 

impact on human performance and workload and the predicted impact of enhanced computer vision algorithms on overall 

system performance (e.g., throughput, operator workload) in the context of realistic missions. Modelers used Command, 

Control, and Communications—Techniques for Reliable Assessment of Concept Execution (C3TRACE) to develop task 

network models for forensic and real-time intelligence gathering. Data from future field experiments can be incorporated into 

the basic modeling environment to validate and extend the preliminary results. This human performance modeling effort was 

conducted as part of a larger human systems integration effort to evaluate the usability and effectiveness of AVAA.

15. SUBJECT TERMS

video analytics, full-motion video, human systems integration, Advanced Video Activity Analytics, AVAA, cognitive 

workload, computational model, human factors, assessment, human performance

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19a. NAME OF RESPONSIBLE PERSON

Michael Barnes

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Contents

List of Figures v

List of Tables v

1. Introduction 1

1.1 Background 1

1.2 Human Performance Modeling 2

2. Method 2

2.1 Evaluations 3

2.2 Scenarios 4

2.2.1 Forensic Scenario 5

2.2.2 Real-Time Scenario 8

2.3 Discrete Event Simulation using C3TRACE 11

2.3.1 Step 1: Define the Personnel 12

2.3.2 Step 2: Define the Functions and Tasks 13

2.3.3 Step 3: Define the Communication Events 16

2.3.4 Step 4: Execute the Analysis 17

2.3.5 Step 5: Analyze Results 17

2.4 Workload 17

2.5 Model Initialization 20

3. Results 21

3.1 Mission Performance 21

3.1.1 Forensic Mission 21

3.1.2 Real-Time Mission 22

3.2 Workload 23

4. Conclusion 25

5. References 27
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Appendix A. Task List 29

Appendix B. Task Diagrams 39

List of Symbols, Abbreviations, and Acronyms 43

Distribution List 45
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List of Figures

Fig. 1 Forensic mission overview ................................................................... 5

Fig. 2 Forensic mission timeline ..................................................................... 6

Fig. 3 AVAA screenshot with V-NIIRS rating graph..................................... 8

Fig. 4 Real-time mission overview ............................................................... 10

Fig. 5 Convoy overwatch timeline ................................................................ 11

Fig. 6 AVAA operators modeled .................................................................. 13

Fig. 7 Top-level network in C3TRACE........................................................ 14

Fig. 8 Forensics mission lower-level task network in C3TRACE................ 15

Fig. 9 Real-time mission lower-level task network in C3TRACE................ 16

Fig. 10 AVAA communication events............................................................ 16

Fig. 11 Workload profile from real-time mission ........................................... 18

List of Tables

Table 1 V-NIIRS................................................................................................ 7

Table 2 Auditory workload scale ..................................................................... 19

Table 3 Cognitive workload scale.................................................................... 19

Table 4 Psychomotor workload scale .............................................................. 20

Table 5 Visual workload scale ......................................................................... 20

Table 6 Forensic mission performance ............................................................ 22

Table 7 Real-time mission performance .......................................................... 23

Table 8 Forensics operator workload............................................................... 24

Table 9 Real-time operator workload .............................................................. 25
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1. Introduction

1.1 Background

This report describes the initial human performance modeling effort that was 

performed in support of the Advanced Video Activity Analytics (AVAA) program. 

The modeling effort focused on task network modeling for the operations of the US

Army intelligence analyst using the AVAA system for forensic (post-hoc) and realtime intelligence gathering. An example forensic mission and an example real-time 

mission were identified and modeled. This human performance modeling effort 

was a task performed as part of the human systems integration support provided to 

the AVAA project by the US Army Research Laboratory (ARL) field element at 

Fort Huachuca, Arizona, and Alion Science and Technology. 

The AVAA system is slated to serve as the sole full-motion video (FMV) 

exploitation capability for the Distributed Common Ground Station-Army. 

AVAA’s objective is to dramatically reduce the analyst’s cognitive workload and 

enable faster and more accurate production of intelligence products (Swett 2013). 

It is envisioned that the completed version of AVAA will assist analysts by 

automatically analyzing, annotating, and organizing massive volumes of video. The 

AVAA architecture consists of a Video Processing and Exploitation Framework 

(VPEF), a Video Data Model (VDM), a VPEF Distributed Server and Client, and 

a VDM Annotated Web Service. The architecture is such that computer vision 

algorithms (CVAs) can be inserted as plug-ins to enable new and emerging 

capabilities to replace or supplement existing capabilities in the framework. 

The goal of the modeling effort is to provide the AVAA team an understanding of 

the current state of the system with respect to the impact on performance and the 

predicted impact of enhanced CVAs on overall system performance (e.g., 

throughput and operator workload). The primary objective is to document the tasks 

performed and use simulation modeling to analyze how different capabilities might 

affect operator workload and task performance during use of the AVAA system in 

realistic missions. Data from future field experiments can be incorporated into the 

basic modeling environment to validate and extend these preliminary results. 

A computer model was developed using Command, Control, and 

Communications—Techniques for Reliable Assessment of Concept Execution 

(C3TRACE) to simulate the tasks that the intelligence analyst performs when using 

AVAA under forensic or real-time situations. C3TRACE is a discrete simulation 

modeling tool that can represent organizations, people, tasks, and communication 

patterns as a function of information flow and information quality. 
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1.2 Human Performance Modeling 

Task network, or discrete event, modeling is a technology that has proven useful for 

predicting human-system performance. In a task network model, human 

performance of an individual performing a function is decomposed into a series of 

subfunctions, which are then decomposed into tasks. The sequence of tasks is 

defined by constructing a task network. Complex task network models can include 

thousands of unique tasks with many interdependencies and complex decision 

logic. 

Task network modeling is an approach to modeling human performance in complex 

systems that has evolved for several reasons. Task analyses organized by task 

sequence are the basis for the task network model. Second, in addition to complex 

operator models, task network models can include sophisticated submodels of 

hardware and software to create a closed-loop representation of the human‒

machine system. Third, task network modeling is relatively easy to use and 

understand. Lastly, it is more cost effective and can be done before, or in concert 

with, running human-in-the loop experiments. 

Because models are run on computer systems, there are no practical limitations on 

the number of trials or simulation runs that can be made. Models can be 

programmed to include provisions for user-input values for parameters and rules 

(e.g., number of operators, type of interface, and environmental conditions), 

thereby allowing different performance conditions to be simulated. This permits the 

investigation of possible changes in operator or crew performance that can occur in 

response to changes in other aspects of the simulated context and objects. Changes 

include the timing of events, variation of performance shaping factors (e.g., stress, 

time available, quality of procedures, fatigue, and crew coordination quality), and 

the availability of crew members (i.e., whether normally staffed or minimally 

staffed). Because models allow users to vary parameters and run conditions for 

multiple iterations, simulations represent a resource for generating information 

about operator and crew performance and providing high statistical power. 

2. Method 

The computational analysis included 3 major steps. First, data were collected on 

system performance (number of videos required), human performance (time and 

accuracy), and workload during the data collection events (Section 2.1). Second, 

representative scenarios were developed to describe realistic tasks involved in a 

forensic mission and a real-time mission. Third, a discrete event simulation model 

was developed. 
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2.1 Evaluations 

Data for the models were collected at 5 different events and from one reference 

(STP 34-35G14SM-TG). The data sources are listed and described as follows: 

1) A Pilot Test Experiment in April 2014: The pilot test was conducted at 

the Experimentation & Analysis Element (EAE) at Fort Huachuca from 

April 14, 2014 to April 17, 2014. Data collection was a joint effort among

ARL, Alion Science and Technology, and AVAA contractors from 

Chenega and EOIR Corporation. There were a total of 6 participants. 

Participants used AVAA during realistic, scenario-based missions to search, 

select, view, and annotate FMV. Participants did one scenario set in the 

Baseline condition and one scenario set in the Video National Imagery 

Interpretability Rating Scale (V-NIIRS) condition that included videos with 

moderate to high V-NIIRS ratings V-NIIRS is a widely used scale to rate 

the interpretability of a given image. A scenario set included 5 tasks, each 

with a different time, date, V-NIIRS range (if applicable), and target to 

locate. 

2) A Full Experiment in June 2014: The first full experiment was conducted 

from June 10, 2014 to June 12, 2014 in the EAE facility at Fort Huachuca. 

The objective was to empirically validate the filtering capabilities (time, 

location, and V-NIIRS ratings) of AVAA for performance improvement and 

for workload reduction. There were a total of 8 participants. Many of the 

techniques used in the pilot study were replicated in the June assessment. 

More specific operational context was given to the analyst during 

instructions and mission tasking for the June event compared with the April 

pilot test. In the June experiment, we collected data on how experienced 

analysts used AVAA to look for and annotate targets (McDermott et al. 

2015). The analysts were given an operational context to read and were 

instructed to find a specific target in each scenario; they were also given a 

list of possible targets that were deemed of intelligence significance and told 

to report their attributes using the annotation tools. 

3) Subject-matter expert (SME) interviews in December 2014: An SME 

was interviewed on December 4‒5, 2014 at Fort Huachuca. The SME was 

currently employed at EOIR Corporation and previously served in the US

Army for 13 years as a Geospatial Intelligence Imagery Analyst (Military 

Occupational Specialty [MOS] 35G). The goal of the SME interview was 

to discuss and obtain data on realistic forensic and real-time scenarios as 
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well as data on the functions and tasks that an intelligence analyst would 

perform in these scenarios. 

4) Real-time demonstration event in January 2016: A demonstration event 

was held at the US Army Aviation and Missile Research, Development and 

Engineering Center Joint Systems Integration Lab (JSIL) at Redstone 

Arsenal in Huntsville, Alabama, from January 11‒15, 2016. Experienced 

analysts watched real-time simulated video using AVAA software. Data 

collection was a joint effort among ARL, Alion Science and Technology, 

AVAA contractors from Chenega and EOIR Corporation, and members of 

the JSIL Human Factors team. 

5) A second full experiment with SME interviews in February 2016: A 

second full experiment was conducted from February 9, 2016 to February 

11, 2016 in the EAE facility at Fort Huachuca. The data collection exercises 

were in 4-h segments, each with 3‒5 Soldiers or staff. There were a total of 

27 participants. All subjects completed one scenario with automated person, 

vehicle, object (PVO) detections and one without PVO. Each scenario had 

eighteen 1-min videos that were grouped into a playlist. In addition to 

performing the tasks in the experiment, participants were questioned about 

the scenarios and tasks for the human performance model, described in 

more detail in Section 2.2. 

Additional task data was obtained from the Soldier’s Manual and Trainer’s Guide 

for Imagery Analyst MOS 35G, Skill Level 1, 2, 3, and 4 (STP 34-35G14-SM-TG). 

2.2 Scenarios 

As documented in STP 34-35G14-SM-TG, 

“Full motion video (FMV) data is received from manned and unmanned aircraft. 

The term FMV refers to motion imagery with a temporal resolution of 30+ frames 

per second. Other video sources may include human intelligence (HUMINT) 

sources, combat camera (COMCAM) operations, commercial news broadcasts 

(such as CNN or the Foreign Broadcast Information Service, FBIS), aircraft 

cockpit video (ACV), and weapon system video (WSV). FMV can provide 

information to analysts on the motion of objects being imaged, multiple 

perspectives of a fixed object, or a combination thereof. Exploitation of FMV can 

be either near real-time or post mission or follow-on exploitation. Near real-time 

reconnaissance, surveillance, and target acquisition can be used to view 

preselected targets and conduct open-ended searches, with the intent to extract and 

disseminate intelligence information in a timely fashion. Post-mission or follow-on 
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exploitation involves the post-real time extraction of useful intelligence information 

from FMV, such as the battle damage assessment (BDA) from ACV or WSV 

imagery.” 

Analysts from Alion Science and Technology worked with an SME with 13 years 

as a Geospatial Intelligence Imagery Analyst (MOS 35G) to develop forensic and 

real-time scenarios that were realistic yet challenging to be used in the human 

performance modeling effort. Function/task analyses were then conducted to 

document the tasks performed during each scenario. 

2.2.1 Forensic Scenario 

The forensic mission starts with human intelligence reports of suspected weapons 

smuggling. The analysis is conducted in non-real time. The area of operations is 

new—there have been no previous assets in the area gathering intelligence. 

Figure 1 illustrates the hypothetical area of operations for the scenario. The border 

is approximately 100 miles long. There are 10 possible border crossings to monitor: 

3 known border crossing checkpoints and 7 suspected crossing points. The airspace 

in the neighboring country is restricted. 

Fig. 1 Forensic mission overview

The Army sends manned and unmanned aircraft to the area to gather intelligence 

about the area. Assets gather intelligence data for 2 weeks at a rate of 16 h/day of 
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aerial coverage. Once the FMV gathering is complete the analyst begins forensic 

analysis of the data using AVAA. The FMV analysis and product development 

(e.g., briefing slides) cover approximately 72 h. 

A high-level timeline is shown in Fig. 2. 

Fig. 2 Forensic mission timeline

AVAA will also include filtering capabilities to help narrow down the total number 

of FMVs to be screened and focus on the FMVs that are most likely to contain 

scenes of interest. One such filter capability is the V-NIIRS filter. The V-NIIRS 

ratings are automatically generated by AVAA and the associated plug-in. The 

ratings and examples of targets that can be identified with each rating are shown in 

Table 1 (Federation of American Scientists 2014). Each frame in the video is given 

a rating; therefore, a single FMV will have a range of V-NIIRS ratings. 
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Table 1 V-NIIRS

V-NIIRS rating Identifiable targets 

0 Interpretability of the imagery is precluded by obscuration, 

degradation, or very poor resolution.

1 

[over 9.0 m ground resolved 

distance (GRD)]

Detect a medium-sized port facility and/or distinguish between 

taxi-ways and runways at a large airfield. 

2

[4.5‒9.0 m GRD]

Detect large static radars.

Detect large buildings (e.g., hospitals, factories). 

3

[2.5‒4.5 m GRD] 

Detect the presence / absence of support vehicles at a mobile 

missile base. 

Detect trains or strings of standard rolling stock on railroad 

tracks (not individual cars).

4

[1.2‒2.5 m GRD] 

Detect the presence of large individual radar antennas.

Identify individual tracks, rail pairs, control towers. 

5

[0.75‒1.2 m GRD]

Identify radar as vehicle-mounted or trailer-mounted. 

Distinguish between SS-25 mobile missile TEL and Missile 

Support Vans in a known support base, when not covered by 

camouflage. 

6 

[0.40‒0.75 m GRD]

Distinguish between models of small/medium helicopters.

Identify the spare tire on a medium-sized truck. 

7

[0.20‒0.40 m GRD] 

Identify ports, ladders, vents on electronics vans. 

Detect the mount for antitank guided missiles (e.g., SAGGER 

on BMP-1). 

8

[0.10‒0.20 m GRD] 

Identify a handheld small-arms munition (e.g., SA-7/14, 

REDEYE, STINGER). 

Identify windshield wipers on a vehicle. 

9 

[ less than 0.10 m GRD] 

Identify vehicle registration numbers on trucks. 

Identify screws and bolts on missile components. 

For the forensic mission the analyst can perform 2 different types of searches on 

the FMV. The first search is a basic date/time search which will return all 224 h of 

FMV collected in the previous 2 weeks. The second type would be to filter the FMV 

using the V-NIIRS rating. Using the filter causes AVAA to return the FMVs that 

have the requested V-NIIRS rating in at least one frame within the video—this 

allows users to filter out low-quality videos. In addition, the V-NIIRS feature 

displays a visualization of the changing V-NIIRS rating over the course of an FMV. 

Figure 3 shows the V-NIIRS rating graph below the video feed. The graph aligns 

with the timeline, and analysts can click on a point in the graph to view video of a 

specific rating. This can be useful in directing analysts to video sections with a 

higher zoom or focus, which may be due to an object of interest in the field of view. 
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Fig. 3 AVAA screenshot with V-NIIRS rating graph

The 224 h of video would be broken into segments that can be filtered. The analyst 

would look at one segment at a time. Data collected at the June 2014 experimental 

event showed that 55% fewer FMVs were evaluated when using the V-NIIRS filter. 

The model was able to use this data to compare the baseline (unfiltered) with VNIIRS data (see Table 1). The analyst chooses an FMV to view. While watching 

the FMV, the analyst looks for and annotates anything that cannot be readily 

explained (e.g., why is someone in this location? why is something moving?) and 

anything unofficial such as an unofficial checkpoint. The analyst repeats this 

process with additional videos. The analyst uses the information collected to build 

a product that shows suspicious activity and persistent inconsistencies. 

2.2.2 Real-Time Scenario 

The real-time mission selected is a convoy overwatch scenario, in which a convoy 

of 20 vehicles carries supplies to a destination 35 miles away. The convoy is 

overwatched by 2 air assets (aircraft). This is a common scenario that analysts are 

involved in during real-time operations. To make the scenario more interesting, 2 

off-nominal but realistic events have been inserted. The first event is small-arms 

fire occurring when the assets are flying to the forward operating base (FOB). The 
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second event is the presence of improvised explosive device (IED) indicators 

detected during the convoy march. 

The scenario assumptions are the following: 

• 2 air assets assigned to the overwatch mission 

• 20 vehicles in convoy—gunners out of hatch with a 50-cal gun 

• Supply convoy for Army/Air Force exchange

• Moving to another province 35 miles away during the day 

• Travel approximately 30 mph

• Day time vehicle spacing used: 100 m between vehicles 

• There is a town 15 min into the route that the convoy will bypass 

• Customer is Special Forces Task Force

• Assume mission, route, and 2‒3 alternate routes have been planned and a 

smaller convoy has driven the route previously 

The scenario can be broken down into 4 phases: 

1) Pre-Mission: During this phase the pre-mission brief is conducted, the 

mission preparation is conducted, and communications are verified. This 

phase ends when the assets take off approximately 1 h before the scheduled 

convoy start time. 

2) En-Route to the FOB: This phase covers the movement of the assets to the 

convoy location. The analyst is watching the video to identify essential 

elements of information or anything suspicious. A small-arms fire event 

occurs 15 min into the flight, which causes the analyst to alert the shift 

manager and give a size, activity, location, unit identification, time, and 

equipment (SALUTE) report. The assets are then delayed to keep eyes on 

the situation and the analyst takes pictures until another asset can be retasked to the area. At that time, the assets continue to the planned FOB. 

3) Convoy Overwatch: The aircraft (air assets) arrive at the FOB and the 

analyst reconnoiters the area. The convoy and air assets move from the FOB 

to a destination in another province approximately 35 miles away. About 

15 min into the mission, the convoy makes a planned detour around a 

village. About 20 miles in, the analyst identifies likely indicators of IEDs. 

The convoy halts, and sets up in formation. An explosive ordnance disposal 

(EOD) team is called. While waiting for the EOD, further checks of the area 
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are conducted to ensure that no secondary devices are emplaced on the road 

and no snipers are concealed nearby. Approximately 1 h later, the EOD 

team arrives and performs a controlled detonation. The convoy then 

continues on the route. Once the convoy has arrived safely, the air assets 

return to base. 

4) Post-Mission: After the mission is complete, the intelligence product is 

produced and release. 

The convoy overwatch overview is shown in Fig. 4 and the mission timeline is 

shown in Fig. 5. The dashed gray line indicates a border. 

Fig. 4 Real-time mission overview
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Fig. 5 Convoy overwatch timeline

2.3 Discrete Event Simulation using C3TRACE 

Discrete event simulations (DESs) use a computer model to describe a process that 

can be expressed as a sequence of events, each with a distinct beginning and end. 

Events can be any part of the process such as scheduled activities or tasks that 
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represent the flow of the process. The tasks are displayed schematically on a 

diagram called the task network diagram, which is the basis of the model. DES 

tools allow users to perform risk analyses by building models that predict possible 

results by substituting a probability distribution for any factor that has inherent 

uncertainty. By using probability distributions, variables have different 

probabilities of different outcomes occurring. During the simulation execution, 

values are sampled at random from the input probability distributions. Each set of 

samples is called an iteration, and the resulting outcome from that sample is 

recorded. DES repeats this assessment hundreds or thousands of times (depending 

on user-specified settings), and the result is a probability distribution of possible 

outcomes. In this way, DES provides a comprehensive view of a variety of possible 

outcomes. It tells analysts not only what could happen, but how likely it is to 

happen. 

C3TRACE is a DES tool that can be used to evaluate different organizational 

concepts of command and control and the impacts of information flow on the 

quality of decisions (Kilduff et al. 2005; Plott et al. 2006). This tool provides the 

capability to represent any organization, the people assigned to that organization, 

the tasks and functions they will perform, and a communications pattern within and 

outside the organization, all as a function of information flow and information 

quality. C3TRACE uses Micro Saint Sharp (Alion Science and Technology,

McLean, VA) a commercially available embedded discrete event task network 

modeling language, as its engine. Task-level information is used to construct 

networks representing the task flow for processing tasks relevant to specific 

missions. C3TRACE is owned and distributed by ARL’s Human Research and 

Engineering Directorate. 

C3TRACE was chosen because it is a versatile tool for modeling environments like 

AVAA, where operators are performing discrete tasks and information is constantly 

being updated. 

The steps required performed to develop the AVAA model in C3TRACE are 

described in the following subsections. 

2.3.1 Step 1: Define the Personnel 

The first step was to define the operators that make up the organization. In 

C3TRACE, this is accomplished by adding sections and operators into a 

hierarchical tree diagram. There is no set limit to the numbers of levels in the 

organizational hierarchy or the number of operators represented. The modeler can 

select personnel attributes for each operator. These attributes include military 

training level, military rank, length of service, battle command experience, and 
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MOS. A modeler can also set various model constraints such as the number of 

simultaneous tasks an operator can perform, workload threshold, and the amount 

of time before a task is dropped permanently. 

For the AVAA model, 5 operators were defined: the Imagery Analyst, Commander, 

Air Surveillance Officer (ASO), Shift Manager, and Operations Officer (S3)

(Fig. 6). 

Fig. 6 AVAA operators modeled

The Imagery Analyst was the only operator modeled in the forensic mission. The 

Imagery Analyst was also the focus of the real-time mission; however, the other 

operators were included for tasks that relate to the analyst’s tasks and impact 

timing. 

2.3.2 Step 2: Define the Functions and Tasks 

The next step was to describe the functions and tasks performed in the scenario. 

Functions and tasks were placed in the workspace and sequenced using a graphical 

point and click interface. Each task has attributes including priority, situation 

awareness level (perception, comprehension, prediction), mode (manual, auto, 

both), decision flag, collaborative flag, timing information (distribution, mean, 

standard deviation), operator assignment (primary operator, alternate primary 

operator, supporting operator(s)), mental workload level, and decision task 

information element weighting. 

Figures 7, 8, and 9 show an example of the top-level task network, the forensics 

mission lower-level task network, and the real-time mission lower level task 

network in C3TRACE. Appendix A contains the task listing and Appendix B 

contains the network diagrams from the AVAA model. 
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Fig. 7 Top-level network in C3TRACE
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Fig. 8 Forensics mission lower-level task network in C3TRACE
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Fig. 9 Real-time mission lower-level task network in C3TRACE 

2.3.3 Step 3: Define the Communication Events 

A key step in the C3TRACE analysis is the definition of the communications 

events. The following data were used to define each communication event: 

• Type of communication 

• Incoming communication frequency and information quality 

• Tasks that are triggered as a result of a communication 

• Communication priority by communication type 

The communication event information provided the scenario “drivers” for the 

analysis. The generated computer simulation model works according to a basic 

"input-throughput-output" scheme. That is, the inputs to the model are 

communication events, which present an information event stream in a frequencydriven time sequence. As these communication events enter the model, tasks are 

triggered and performed in a pattern that reflects the logic for task branching and 

interrupt priorities. The types of communication events that can be defined are 

digital (email, whiteboard, maps, sensors), voice, face-to-face, written, haptic, 

radio, and intercom. 

For the AVAA model, 2 communication events were defined (Fig. 10). There is 

one event to initialize the simulation variables and one event to start the small-arms 

fire in the real-time mission. The StartMission event occurs at time 0 and the 

SmallArmsFire starts when the En-Route to FOB phase begins. 

Fig. 10 AVAA communication events
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2.3.4 Step 4: Execute the Analysis 

Once the analysis had been defined, C3TRACE automatically generated and 

executed a DES model that represents the organization, tasks, and communications 

entered by the analyst. Each AVAA scenario was executed 100 times for each 

mission. 

2.3.5 Step 5: Analyze Results 

The last step was to analyze the results. C3TRACE generated several categories of 

output that include the following: 

• Human performance metrics 

• Sensor-to-shooter data 

• Information driven decision quality 

Specifically, C3TRACE provides reports on efficiency measures of operator 

utilization, task drops and suspensions, operator task completions, sensor-toshooter timelines, and probability of making a good decision. These measures 

together provide human performance analysts with a tool that can be used to 

examine the process of battle command. 

2.4 Workload 

For each task, workload values were assigned that reflected the amount of effort 

the operator will have to expend to perform the task. Workload values for each task 

in the model were obtained from observation of operators using the AVAA system 

during the pilot and data collection events and from SME interviews. During the 

simulation, C3TRACE calculates how much workload each operator was 

experiencing throughout the mission. This can be used to determine whether the 

operators were overloaded. 

The workload in C3TRACE is tracked using the Visual, Auditory, Cognitive, and 

Psychomotor (VACP) demand vector and a task interference matrix. Workload is 

estimated by task for each of the 4 “channels” (i.e., visual, auditory, cognitive, and 

psychomotor) and then an additional score is added to account for interference 

between resources. As the model runs, workload is tracked across all 4 channels, 

the interference conflict, and an overall score is calculated and tracked for each 

operator. At the conclusion of the run, the built-in “Operator Workload” report 

displays the value of workload versus time (Fig. 11), which can be used to identify 

workload "peaks" during the operational mission. 
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18

Fig. 11 Workload profile from real-time mission

Workload theory is based upon the idea that every task a human performs requires 

some work. Usually a task comprises several different types of work, such as visual 

or cognitive. As an example, consider a task like steering a car. This task will have 

some visual work (watch where you are going), some cognitive work (decide if you 

are turning enough), and some psychomotor work (rotate the steering wheel). The 

workload theory in C3TRACE helps modelers assign values representing the 

amount of effort that must be expended in each channel to perform the task. To 

assist the modeler, C3TRACE displays a list of scale values and descriptors for 

each channel. These scales are taken directly from Bierbaum et al. (1989) and are 

shown below. 

This theory also hypothesizes that if an operator is doing 2 or more tasks at once, 

the workload levels are additive within channels, across tasks. For example, if an 

operator was doing 2 tasks at once, one with a psychomotor load of 2.6 and one 

with a psychomotor load of 4.6, then C3TRACE would record a psychomotor score 

of 2.6 + 4.6 = 7.2 for the time that the 2 tasks were being performed together. Any 

time a workload value exceeds 7 for VACP, the person can be considered to have 

exceeded their workload capacity for that particular resource (McCracken and 

Aldrich 1984). In addition, some researchers believe that when the overall workload 
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19

value (V+A+C+P) exceeds 40, a person is considered to have exceeded their overall 

workload capacity (Reid and Colle 1988). The value of each workload channel 

throughout the mission is displayed in a graphical and tabular format in 

C3TRACE’s “Operator Workload” report. This graph can be used to pick out the 

times during the mission that the workload “peaked”. The analyst can then use the 

“Operator Details Report” to determine what the operator was doing during these 

peaks to pinpoint the task or combination of tasks that contributed to high workload 

for each operator. 

VACP Scales 

Auditory Workload Scale. The auditory workload scale embedded in the VACP 

missions in C3TRACE is shown in Table 2.

Table 2 Auditory workload scale

Scale value Auditory scale descriptor

0.0 No Auditory Activity 

1.0 Detect/Register Sound (detect occurrence of sound) 

2.0 Orient to Sound (general orientation/attention) 

4.2 Orient to Sound (selective orientation/attention) 

4.3 Verify Auditory Feedback (detect occurrence of anticipated sound) 

4.9 Interpret Semantic Content (speech) 

6.6 Discriminate Sound Characteristics (detect auditory differences) 

7.0 Interpret Sound Patterns (pulse rates, etc.) 

Cognitive Workload Scale. The cognitive workload scale embedded in the VACP 

missions in C3TRACE is shown in Table 3.

Table 3 Cognitive workload scale

Scale value Cognitive scale descriptor 

0.0 No Cognitive Activity 

1.0 Automatic (simple association) 

1.2 Alternative Selection 

3.7 Sign/Signal Recognition 

4.6 Evaluation/Judgment (consider single aspect) 

5.3 Encoding/Decoding, Recall 

6.8 Evaluation/Judgment (consider several aspects) 

7.0 Estimation, Calculation, Conversion 

Psychomotor Workload Scale. The psychomotor workload scale embedded in the 

VACP missions in C3TRACE is shown in Table 4.
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20

Table 4 Psychomotor workload scale

Scale value Psychomotor scale descriptor 

0.0 No Psychomotor Activity 

1.0 Speech 

2.2 Discrete Actuation (button, toggle, trigger) 

2.6 Continuous Adjustive (flight control, sensor control) 

4.6 Manipulative 

5.8 Discrete Adjustive (rotary, vertical thumbwheel, lever position) 

6.5 Symbolic Production (writing) 

7.0 Serial Discrete Manipulation (keyboard entries) 

Visual Workload Scale. The visual workload scale embedded in the VACP 

missions in C3TRACE is shown in Table 5.

Table 5 Visual workload scale

Scale value Visual scale descriptor 

0.0 No Visual Activity 

1.0 Visually Register/Detect (detect occurrence of image) 

3.7 Visually Discriminate (detect visual differences) 

4.0 Visually Inspect/Check (discrete inspection/static condition) 

5.0 Visually Locate/Align (selective orientation) 

5.4 Visually Track/Follow (maintain orientation) 

5.9 Visually Read (symbol) 

7.0 Visually Scan/Search/Monitor (continuous/serial inspection, multiple 

conditions) 

2.5 Model Initialization 

Model input parameters were placed into a C3TRACE macro that allows users to 

easily set their values: 

• Mission type: either “forensic” or “real time” 

• Forensic mission parameters method: search method used and number of 

hours of FMVs returned 

• Real-time mission parameter: Using AVAA to collect screenshots and 

produce product, true or false 

For the forensic mission, if the analyst performs a basic date/time search the results 

will have all 224 h of FMV gathered in the area over the previous 2 weeks. The 

experimental data collected in June 2014 (McDermott et al. 2015) showed that if 

the imagery analyst uses the AVAA V-NIIRS quality filter, 55% less video was 

returned. However, C3TRACE/human performance analysts can change this 

parameter, run the model, and examine impacts on mission performance. 
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21

For the real-time mission, if the analyst uses the AVAA interface it will take longer 

to capture that screen image than using the traditional screenshot. Using AVAA, 

the process to get a screenshot is 1) click the screenshot button, 2) draw a square 

around the portion of screen to capture, 3) release the mouse, 4) right click on the 

newly created file, and 5) rename the file. If AVAA is not used, the intelligence 

analyst hits the screenshot button and the whole screen is saved on the hard disk. 

When creating the product, it is predicted that using AVAA would be faster, since 

all of the screenshots would be available from the AVAA interface and the analyst 

would not have to search the hard disk for them. With AVAA, analysts make 

specific screenshots with meaningful names so they can be used easily to create 

reports and products compared with the non-AVAA interface where screenshots 

must be reviewed one by one. AVAA also allows for premade product templates to 

be used. 

3. Results 

3.1 Mission Performance 

3.1.1 Forensic Mission 

For the forensic mission, the model was executed 100 times with the basic date/time 

search and 100 times with a V-NIIRS filtered search. The input probability 

distribution timing data for each individual task was obtained from the SME 

interviews and data collection exercises. Predicted performance data were collected 

for the following: 

• Number of hours of FMV returned from the search 

• Time required for the analyst to perform the FMV analysis 

• Time required for the analyst to prepare the product 

• Total mission time 

Results are show in Table 6. The results predicted that if using the V-NIIRS filter 

reduced the amount of FMV to view by approximately 55% (McDermott et al. 

2015), then the amount of time to analyze the video would be reduced by 

approximately 70%, the total mission time would be reduced by approximately 

15%, and the time for the analyst to perform the forensic analysis would be reduced 

by 70%. 
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22

Table 6 Forensic mission performance

Basic date/time

search

(h)

Date/time/geographic

location/V-NIIRS

search (h)

Difference

FMV to 

analyze 

Average/standard 

deviation 

223.62/10.89 101.11/4.47 54.7%

Maximum 245 111 134 h

Minimum 197 92 105 h

Analyst 

forensic 

analysis 

Average/standard 

deviation 

88.62/11.72 26.29/3.51 70.3%

Maximum 100.3 50.85 49.44 h

Minimum 71.57 24.13 47.4 h

Analyst 

prepares

product 

Average/standard 

deviation 

0.39/0.088 0.38/0.086 3.8%

Maximum 0.63 0.58 0.043

Minimum 0.17 0.17 0

Total 

mission 

Average/standard 

deviation 

428.03/11.78 365.66/3/63 14.6%

Maximum 439.86 390.99 48.87

Minimum 411.49 362.82 48.67

3.1.2 Real-Time Mission 

The real-time mission was also executed 100 times for the case where AVAA was 

used to collect screenshots/make products and 100 times for the case where AVAA 

was not used. Using AVAA would save approximately 6 min in a 9- to 10-h mission 

(1.1%). Using AVAA taking screenshots takes longer, but producing the product is 

faster. For the real-time mission, predicted performance data were collected for 

each phase of the mission as show in Table 7. 
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23

Table 7 Real-time mission performance

Phase

Using AVAA for 

screenshots/product

time required

(h)

Not using AVAA

time required

(h)

Difference

(AVAA

advantage)

(h)

Premission 

Average/standard 

deviation 

1.49/0.06 1.48/0.06 ‒0.01

Maximum 1.62 1.64 0.02

Minimum 1.35 1.31 ‒0.04

En-route 

to FOB 

Average/standard 

deviation 

2.61/0.02 2.61/0.01 0.00

Maximum 2.65 2.63 ‒0.02

Minimum 2.59 2.57 ‒0.02

Convoy 

overwatch 

Average/standard 

deviation 

2.66/0.06 2.67/0.04 0.01

Maximum 2.8 2.78 ‒0.02

Minimum 2.54 2.58 0.04

Postmission 

Average/standard 

deviation 

1.56/.21 1.67/0.19 0.11

Maximum 2.04 2.07 0.03

Minimum 1.09 1.23 0.12

Total 

mission 

Average/standard 

deviation 

9.48/.24 9.58/0.21 0.10

Maximum 10.08 10.07 ‒0.01

Minimum 8.84 9.08 0.24

3.2 Workload 

Analysis of the workload data produced by the model for the forensic mission 

predicted that workload does not produce an overload situation—where workload 

exceeds 28 (7-point scale, 4 resources, 7×4=28) at any given point in the scenario. 

The data are shown in Table 8. The maximum predicted workload occurred when 

the imagery analyst was performing the task “Prepare any imagery derived products 
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24

(IDP), geospatial intelligence (GEOINT) products”. The individual task workload 

estimates are based on metrics developed from previous estimates generated by 

operators of the Black Hawk helicopter and have been used successfully on multiple 

military systems (Bierbaum et al. 1989). 

Table 8 Forensics operator workload

Visual Auditory Cognitive Psycho 

-motor

Overall 

workload

35G – Basic date/time search 

 Average workload 2.52 0 1.79 1.87 6.19

 Standard deviation 2.84 0 2.53 2.39 6.93

 Maximum 7.0 0 6.8 7.0 19.7

35G – Date/time/geographic 

location/V-NIIRS search 

 Average workload 2.42 0 1.71 1.75 5.88

 Standard deviation 2.75 0 2.48 2.34 6.75

 Maximum 7.0 0 6.8 7.0 19.7

The workload data generated by the model were compared to the workload ratings 

from the Fort Huachuca evaluation study from June 2014 (McDermott et al. 2015). 

The Fort Huachuca study captured NASA-TLX ratings of perceived workload after 

each mission. Neither workload metric showed a large difference between 

workload with V-NIIRS and without V-NIIRS. The overall weighted NASA-TLX 

rating with the V-NIIRS was slightly higher than without V-NIIRS (9.33 workload 

rating with V-NIIRS compared to 8.42 workload rating without V-NIIRS) while 

the model derived workload was slightly lower with V-NIIRS. Because the scales 

of the model-derived workload and the NASA-TLX workload differ, the relative 

differences from the non-V-NIIRSs condition were calculated. In the modelpredicted data, workload is 5% lower with V-NIIRS and in the NASA-TLX 

(empirical) data, workload is 10% higher with V-NIIRS. 

In summary, the forensic workload data may differ slightly between the model and 

the experimental data but the overall story is that workload is not in an overload 

state and is not considerably different with or without V-NIIRS. The observed 

differences could be attributed to different measurement constructs of workload. 

However, none of the 3 metrics used (C-3TRACE, NASA-TLX, and physiological 

measures) showed significant overload for the 2 AVAA experimental conditions, 

suggesting that workload was not a serious issue for the forensic tasks. 
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25

Workload data for the real-time mission is shown in Table 9. Analysis of the 

workload data produced by the model for the forensic mission predicted that 

workload does not produce an overload situation. 

Table 9 Real-time operator workload

Visual Auditory Cognitive Psycho 

-motor

Overall 

workload

Imagery Analyst 

 Average workload 1.99 0.63 2.56 0.71 5.89

 Standard deviation 2.74 1.59 2.93 1.49 6.45

 Maximum 7 4.9 7 7 19.7

ASO 

 Average workload 1.97 0.60 2.51 0.64 5.72

 Standard deviation 2.78 1.56 2.94 0.99 6.36

 Maximum 7 4.9 6.8 2.6 14.8

Shift Manager 

 Average workload 0.79 1.96 2.11 0.62 5.48

 Standard deviation 1.75 2.4 2.8 0.71 5.97

 Maximum 5.9 4.9 6.8 2.2 14.9

S3 

 Average workload 0 2.45 3.4 0.5 6.35

 Standard deviation 0 2.45 3.4 0.5 6.35

 Maximum 0 4.9 6.8 1 12.7

4. Conclusion 

A baseline human performance model was developed under this effort that 

illustrates: 

• A realistic forensic mission and real-time mission showing the tasks that 

would be performed by the Imagery Analyst (35G) using the AVAA system 

• Predicted mission performance and workload from representative forensic 

and real-time operations 

The preliminary conclusion from executing the model and observing operators 

using the AVAA system indicate that neither the forensic or real-time mission are 

expected to produce an overloaded workload situation for the imagery analyst. In 

the forensic mission, if the use of the V-NIIRS filter reduces the amount of FMV 

to view by approximately 55%, then the amount of time to analyze the video would 

be reduced by approximately 70.3% and the total mission time would be reduced 
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26

by approximately 14.6%. In the real-time mission, using AVAA takes more time to

collect screenshots, but saves time creating end-of-mission products. 

The AVAA architecture is being developed to support the inclusion of complex 

CVAs such as object detection, classification, and tracking, face detection, license 

plate detection, change detection, precision georectification, video stabilization, 

and quality filters. 

Since AVAA is a system in development, many of the capabilities that will 

eventually be required when the system is fielded have not yet been completed and 

fully integrated. For the simulation to be as realistic as possible, when data become 

available, these capabilities and associated tasks should be incorporated in the 

model. 
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27

5. References 

Bierbaum C, Szabo S, Aldrich T. Task analysis of the UH-60 mission and decision 

rules for developing a UH-60 workload prediction model. Fort Rucker (AL):

Army Research Institute Aviation (US) R&D Activity; 1989. 

Federation of American Scientists. National image interpretability rating scales. 

[accessed 2014]. http://fas.org/irp/imint/niirs.htm, 2014.

Kilduff P, Swoboda J, Barnette D. Command, control, and communications: 

techniques for the reliable assessment of concept execution (C3TRACE) 

modeling environment: the tool. Aberdeen Proving Ground (MD): Army 

Research Laboratory (US); 2005. Report No.: ARL-MR-0617. 

McCracken JH, Aldrich TB. Analyses of selected LHX mission functions: 

Implications for operator workload and system automation goals. Fort Rucker

(AL): Army Research Institute Aviation Research and Development Activity; 

1984. Report No.: Technical Note ASI479-024-84. 

McDermott P, Plott B, Reis A, Touryan J, Barnes M, Schweitzer K. Advanced 

Video Activity Analytics (AVAA): human factors evaluation. Aberdeen 

Proving Ground (MD): Army Research Laboratory (US); 2015. Report No.: 

ARL-TR-7286.

Plott B, Quesada S, Kilduff P, Swoboda J, Allender L. Using an information-driven 

decision making human performance tool to assess command, control, and 

communication issues. Proceedings of the Human Factors and Ergonomics 

Society Annual Meeting. 2006;48(2):2396–2400. 

Reid GB, Colle HA. Critical SWAT values for predicting operator workload. 

Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

1988;32(1):1414–1418. 

Soldier’s manual and trainers guide for imagery analyst 35G skill level 1, 2, 3 and 

4 (2008). Report No.: STP 34-35G14-SM-TG. 

Swett B. Advanced Video Activity Analytics (AVAA) overview. AVAA 

preliminary design review presentations. Lorton, VA; 2013 Nov 6‒7. 
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29

Appendix A. Task List1

 

This appendix appears in its original form, without editorial change.
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30

Forensic Task List 

Mission Task Subtasks Performed By Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Auditory Cognitive Psychomotor 

Forensics 

Forensics Mission: 

Suspected 

Weapons 

Smuggling in 

New Area 

Forensics 

Mission arrives -

requirement to gather 

intel 

about unknown area, 

reports of suspected 

weapons 

trafficking 30 5 0 0 0 0 

Forensics 

Assets sent to area to 

gather FMV and other 

data 150 30 0 0 0 0 

Forensics Assets gather data 960 0 0 0 0 0 

Forensics 

Analyst begins 

forensic analysis Imagery Analyst 10 1 5.4 0 1 0 
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31

Mission Task Subtasks Performed 

By 

Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Auditory Cognitive Psychomotor 

Forensics 

Review 

Exploitation 

Requirements 

Imagery 

Analyst 8 1.5 5.9 0 5.3 0 

Forensics 

Obtain imagery and 

geospatial data 

Imagery 

Analyst 5 2 3.7 0 5.3 4.6 

Forensics 

Conduct Analysis 

and Manipulation of 

FMV 

Forensics Begin Search 0 0 0 4.9 0 0 

Forensics Basic DateTime 

Search Method 

Forensics 

Set Date and 

Time 

Parameters 

Imagery 

Analyst 1 0.2 1 0 1 2.2 

Forensics Query 

System Files 

Imagery 

Analyst 2 0.5 1 0 0 2.2 

Forensics 

Manually 

Review & 

Sort Files 

Imagery 

Analyst 1 0.15 3.7 0 1 2.2 

Forensics 

Filter on 

Date,Time, and 

Geographic location 
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32

Mission Task Subtasks Performed By Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Auditory Cognitive Psychom

otor 

Forensics 

Set 

DateTime 

and Geo 

Parameters 

Imagery Analyst 

2 0.5 3.7 0 1.2 2.2 

Forensics Query 

System Files 

Imagery Analyst 2 0.5 1 0 0 2.2 

Forensics Review Files Imagery Analyst 0.5 0.15 3.7 0 0 2.2 

Forensics 

Analyze Video 

(play, pause, fast 

forward) 

Imagery Analyst 

 7 0 6.8 2.2 

Forensics Make Annotations Imagery Analyst 30 5 5 0 4.6 4.6 

Forensics 

Write quick notes 

at the end of each 

video 

Imagery Analyst 

30 5 5.9 0 1 6.5 

Forensics Analyst Off Shift 720 0 0 0 0 0 

Forensics Review End 0 0 0 0 0 0 

Forensics 

Prepare any 

imagery derived 

products (IDP), 

geospatial 

intelligence 

(GEOINT) 

products (301-

35G-1305), and 

reports in 

accordance with 

unit SOP. Imagery Analyst 20 5 5.9 0 6.8 7 

Forensics Deliver Product Imagery Analyst 3 2 1 0 1 2.2 
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33

Real-Time Task List 

Mission Phase Task Performed 

By 

Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Audito 

ry 

Cognit

ive 

Psycho 

motor 

RealTime Real Time Mission: 

Convoy Overwatch 

RealTime Pre-Mission 

RealTime ASO comes on shift 

(1-2 hrs before a/c in air) ASO 1 0 1 0 1 0 

RealTime 

Pre-mission brief (Customer, Task Force 

and ASO) 30 5 1 4.9 6.8 1 

RealTime 

Analyst calls ASO to verify 

information and make sure on "the 

same page" 

Imagery 

Analyst 15 3 0 4.9 4.6 1 

RealTime Verify comms working (mIRC and 

cameras) 10 2.5 4 1 3.7 1 

RealTime 

Analyst tells Shift 

Manager 

"Good to Go" 

Imagery 

Analyst 1 0.25 0 4.9 1 1 

RealTime ASO and a/c Take-Off ASO 3 1 5.4 0 6.8 2.6 

RealTime Continue Preparation Imagery 

Analyst 60 0 5.9 0 7 0 

RealTime En-Route to FOB 

RealTime Analyst watches video feed while en

-route Imagery 

Analyst 60 0 7 0 6.8 0 
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Mission Phase Task Performed By Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Audito 

ry 

Cognit

ive 

Psycho 

motor 

RealTime Small Arms Fire Occurs 105 0 0 0 0 0 

RealTime 

Analyst sees small arms 

fire 

(customer and ASO also sees) Imagery Analyst 0.5 0.2 5 0 5.3 0 

RealTime 

Analyst alerts Shift 

Manager – 

Gives SALUTE report 

Imagery Analyst 

1 0.2 4 1 5.3 1 

RealTime Shift Manager calls S3 Shift Manager 1 0.2 1 4.9 5.3 1 

RealTime Analyst takes still snapshots Imagery Analyst 15 1 5 0 3.7 2.2 

RealTime ASO keeps "eyes on" activity ASO 2 1 5.4 0 6.8 2.6 

RealTime 

S3 calls for temporary 

dynamic retasking - stay on 

activity until another asset 

arrives on scene S3 1 0.3 0 4.9 6.8 1 

RealTime A/C circles area 15 0 0 0 0 0 

RealTime New asset arrives 0 0 0 0 0 0 

RealTime Handoff to new asset 3 0.5 0 4.9 6.8 1 

RealTime A/C proceed to FOB (speed 

up if necessary) 30 0 0 0 0 0 
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35

Mission Phase Task Performed By Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Audito 

ry 

Cognit

ive 

Psycho 

motor 

RealTime 

Analyst asks the customer if 

they want the analysis of 

small arms fire right away or 

post mission Imagery Analyst 1 0.2 0 4.9 5.3 1 

RealTime Customer wants analysis 

right away 0.5 0.05 0 1 5.3 0 

RealTime 

Shift Manager assigns 

product 

development to another 

analyst Shift Manager 2 0.5 0 4.9 1.2 1 

RealTime Arrive at FOB 0 0 0 0 0 0 

RealTime Convoy Overwatch Mission 

RealTime A/C on station 0 0 0 0 0 0 

RealTime Recon area until departure 

time 9 0.3 7 0 3.7 0 

RealTime Maintain eyes on convoy at 

all times 39 2 7 0 6.8 0 

RealTime Convoy heads out 30 0 0 0 0 0 

RealTime Planned detour around town 9 0 0 0 0 0 

RealTime Analyst ID's possible IED 

threat on FMV 

Imagery Analyst 0 0 4 0 6.8 0 

RealTime 

Analyst calls for further 

investigation and looks to 

match signatures 

Imagery Analyst 

0 0 4 0 3.7 1 
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36

Mission Phase Task Performed By Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Audito 

ry 

Cognit

ive 

Psycho 

motor 

(straight dirt line could 

mean command line 

explosive) 

RealTime Shift Manager Call EOD unit 

and S3 Shift Manager 5 0.5 0 4.9 1 1 

RealTime 

Customer calls for the convoy 

to 

stop and get in tactical 

position 

(park vehicles in a formation) 2 0.5 0 4.9 4.6 1 

RealTime Convoy moves into formation 10 0 0 0 0 0 

RealTime Wait for EOD team 30 0 0 0 0 0 

RealTime EOD Team Arrives 0 0 0 0 0 0 

RealTime 

a/c circle area and look for 

activity in area, buildings 60 0 7 0 3.7 2.6 

RealTime EOD Team investigates 55 0 0 0 0 0 

RealTime 

EOD asks and obtains 

permission to blw 

(controlled 

detonation) 5 0 0 0 0 0 

RealTime blow potential IED 6 0 0 0 0 0 

RealTime 

Zoom in for battle damage 

assessment (BDA), create 

video of 

Imagery Analyst 

6 0 5.4 0 6.8 2.6 
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37

Mission Phase Task Performed By Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Audito 

ry 

Cognit

ive 

Psycho 

motor 

EOD activity – make pre/post report 

RealTime Convoy continues on mission 20 2 0 0 0 0 

RealTime Maintain eyes on convoy at 

all times 20 2 7 0 6.8 0 

RealTime Convoy arrives at destination 0 0 0 0 0 0 

RealTime a/c head back 60 0 0 0 0 0 

RealTime a/c lands 3 0 0 0 0 0 

RealTime Analyst watches video feed 

while en-route 

Imagery Analyst 60 0 7 0 6.8 0 

RealTime Post Mission 

RealTime ASO send hi

-res snapshots to 

analyst ASO 3 0.5 1 0 1 2.2 

RealTime Analyst begins producing 

report 

Imagery Analyst 12 2 5.9 0 6.8 7 

RealTime analyst/team leader complete 

product 

Imagery Analyst 45 10 5.9 0 6.8 7 

RealTime Send product to Shift 

Manager 

Imagery Analyst 1 0.5 1 0 1 2.2 

RealTime 

Shift Manager makes minor 

adjustments, QA/QC and 

approves Shift Manager 30 5 5.9 0 6.8 2.2 

Mission Phase Task Performed By Mean 

Time 

(min) 

Std 

Dev 

(min) 

Visual Audito 

ry 

Cognit

ive 

Psycho 

motor 

RealTime analyst posts product in 

GETS repository 

Imagery Analyst 2 0.3 1 0 1 2.2 

RealTime Shift OIC releases product in 

GETS 1 0.2 1 0 1 2.2 
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Appendix B. Task Diagrams2

 This appendix appears in its original form, without editorial change.
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40

Forensic Mission 

Top Level Network 

Start Function 
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41

Conduct FMV Analysis 

Pre-Mission 

Real Time 

En-Route to 
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42

Convoy Overwatch 

Post 
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List of Symbols, Abbreviations, and Acronyms

ACV aircraft cockpit video

ASO Air Surveillance Officer

AVAA Advanced Video Activity Analytics

BDA battle damage assessment

C3TRACE Command, Control, and Communications—Techniques for 

Reliable Assessment of Concept Execution

CVA computer vision algorithm

DES discrete event simulation

EAE Experimentation & Analysis Element

EOD explosive ordnance disposal

FMV full-motion video

FOB forward operating base

GEOINT geospatial intelligence

GRD ground resolved distance

IDP imagery derived products

IED improvised explosive device

JSIL Joint Systems Integration Lab

MOS Military Occupational Specialty

PVO person, vehicle, object

S3 Shift Manager, and Operations Officer

SALUTE size, activity, location, unit identification, time, and equipment

SME subject-matter expert

VACP Visual, Auditory, Cognitive, and Psychomotor

VDM Video Data Model

V-NIIRS Video National Imagery Interpretability Rating Scale
Approved for public release; distribution is unlimited.

44

VPEF Video Processing and Exploitation Framework

WSV weapon system video
Approved for public release; distribution is unlimited.

45

1 DEFENSE TECHNICAL

(PDF) INFORMATION CTR

DTIC OCA

2 DIR ARL

(PDF) IMAL HRA 

RECORDS MGMT

RDRL DCL

TECH LIB

1 GOVT PRINTG OFC

(PDF) A MALHOTRA

1 DIR ARL

(PDF) RDRL HRM A

M BARNES
Approved for public release; distribution is unlimited.

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