Pattern of Life from WAMI Objects Tracking based on ...

Invited Paper

Pattern of Life from WAMI Objects Tracking based on ContextAware Tracking and Information Network Models Jianjun Gaoa, Haibin Lingb, Erik Blaschc, Khanh Phamd, Zhonghai Wanga, Genshe Chena a

Intelligent Fusion Technology, Inc. Germantown, MD 20876 b Temple University,Philadephia, PA 19122 c Air Force Research Lab, Rome, NY, 13441 d Air Force Research Lab, Kirtland AFB, NM, 87117

ABSTRACT With the emergence of long lasting surveillance systems, e.g., full motion video (FMV) networks and wide area motion imagery (WAMI) sensors, extracting targets’ long term pattern of life over a day becomes possible. In this paper, we present a framework for extracting the pattern of life (POL) of targets from WAMI video. We first apply a context-aware multi-target tracker (CAMT) to track multiple targets in the WAMI video and obtain the targets’ tracklets, traces, and the locations, from surveillance information extracted from the targets' long-term trajectories. Then, entity networks propagate over time are constructed with targets’ tracklets, traces, and the interested locations. Finally, the entity network is analyzed using network retrieving technique to extract the POL of interested targets. Keywords: Pattern of life, WAMI, multi-target tracking, entity networks, pattern extraction.

1.

INTRODUCTION

“Pattern of life” or “behavior pattern” describes a recurrent (e.g., normalcy) way of acting by an individual or group toward a given object or in a given situation. In this paper, we use the term pattern of life (POL) instead of behavior pattern. Usually, one has a specific POL, and this POL is repeatable. The POL can be divided into two categories, shortterm POL (for example when one person sits on his/her chair, he/she usually put his/her arms, hands and feet at about the same locations) and long-term POL (for example, every day, one people gets up at 7:00am, takes a shower and then has breakfast, goes to work at 8:00am, works between 9:00am and 12:30pm, has lunch at 12:30pm, works between 1:00pm and 5:00pm, comes back home at 6:00pm, has dinner at 6:30pm, watches TV or reads between 7:30pm and 9:30pm, and goes to bed at 10:00pm). For its uniqueness, POL can be used to detect a specific target in a group of candidates, or find the target’s abnormal action with respect to his/her/its normal pattern. An example is detecting a criminal in a group of residents based on his/her daily POL. Normal residents go to his/her working location, and work there for about 8 hours; while a criminal (e.g., drug dealer) drives to a drug-house/dealing-location, sells his/her drugs and then leaves the location. He/she may drive to the next dealing location to sell his/her drugs, drive back to home or go shopping. The drug dealing locations are known to police, and if a person goes to multiple drug-dealing locations with a fixed patterns, he/she may be a drug dealer with a high probability. It is assumed the normal residents may go to one of these locations for work, but will not go to identified locations of possible criminal activities. POL has been used for military intelligence as early as the 1960s [9]. After 2010, POL has been used to decide whether or not should be engaged . This has been reported by Los Angeles Times [16] as follows: “The CIA received secret permission to attack a wider range of targets, including suspected militants whose names are not known, as part of a dramatic expansion of its campaign of drone strikes in Pakistan's border region, according to current and former counter-terrorism officials. [16] “The expanded authority, approved two years ago by the Bush administration and continued by President Obama, permits the agency to rely on what officials describe as "pattern of life" analysis, using evidence collected by surveillance cameras on the unmanned aircraft and from other sources about individuals and locations.” [16]

Signal Processing, Sensor Fusion, and Target Recognition XXII, edited by Ivan Kadar, Proc. of SPIE Vol. 8745, 87451K · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2015612 Proc. of SPIE Vol. 8745 87451K-1 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

In 1960s, the computational abilities were not powerful enough to support the POL analysis and subsequent POL analysis was analyzed by humans (which resulted in long delays). An accurate POL analysis requires a great deal of processing power and observations over a long time period such as days. Fortunately, the advances in computer techniques, e.g., multi-kernel central processing unit (CPU) and the general-purpose graphics processing unit (GPGPU), make timely POL analysis possible with a combination of big data and analysis input. Researchers have applied computers to analyze target behaviors both for short term and long term POL 0. Continuous surveillance systems, e.g., full motion video (FMV) networks [10] and wide area motion imagery (WAMI) video sensors [11], can continuously monitor a region for a long time period. Specifically, a WAMI sensor can cover a city sized area and provides surveillance video with approximately 0.5 meters resolution and about 1 or 2 frames per second [7]. For its large coverage area and long dwell time (hours to weeks), the WAMI video allows one to observe many dynamic phenomena that cannot be obtained using satellite or a single street fixed video imaging systems. The WAMI information can complement localication signals from targets for patterns of life which is evident from the recent advances in cell-phone technology [14]. In satellite surveillance systems, though the coverage area is large enough, the image resolution is too low to identify a target such as a car. In a street fixed video imaging system, though the resolution of the obtained video is high, its field of view is too small to obtain a target’s dynamic phenomena for long time duration. In a WAMI video, we can track a large number of vehicles from their initial points to the end points within an urban environment for weeks. From the obtained vehicle tracks, we can extract higher-level information, such as vehicle activities, situation awareness, the pattern of traffic on a free way, the pattern of activity at a specific location (e.g., a supermarket or a drug-house), and POL of an interested vehicle/target, etc. Researchers have used WAMI video to exploit target activities (e.g., multiple vehicle meetings [15]) and the attributes of the interested locations and vehicle tracks [16]. But we only found limited research on the high level intelligence analysis based on WAMI data. In this paper, we present a frame work for extracting target POL from WAMI video. As shown in Figure 1, the system inputs include two parts, one is the WAMI video and the other is the information database. The information database is initially provided by police or other information sources, and is continuously updated as the system processes observations. It provides the interested areas, target locations and object identifications. For example, the interested areas and locations are those that have been visited by at least one drug dealers. And the interested identifications are those cars/trucks that have been identified previously by police or the information system. Using a context-aware multi-target tracker (CAMT), we track the targets in the WAMI video and obtain targets’ tracks, the locations that these targets have been visited, the time of arrival and the time duration the targets stayed at the interested locations. Using targets’ tracks and the locations that the targets have visited, we construct an entity network, which depicts the relationship between multiple targets via target’s correlation. The correlation between multiple targets is calculated using the overlapping time that these targets stay at the same location. When a specific target and a known entity (e.g., possible drug dealer) have high correlation, i.e., their correlation is larger than a threshold which indicates that more attention should be paid to this target’s POL. Using the data of a target’s tracks, locations visited, the time of arrival and the time duration it stayed at each location obtained through a long time duration (e.g., multiple days or multiple weeks), a target’s POL is obtained. The rest of the paper is organized as follows: Section 2 introduces the context-aware multi-target tracker; Section 3 deals with interested area localization; Section 4 presents the entity network construction; Section 5presents the pattern of life recording; and, Section 6 concludes the paper.

2.

CONTEXT-AWARE MULTI-TARGET TRACKER

Fundamentally speaking, the problem of visual multi-target tracking is a target estimation and association problem. We roughly divide the tracking framework into three sequential parts: background modeling, target detection and estimation, and target association. In this section, we will briefly introduce the target association part given the target estimation and tracking has been reported elsewhere [18]. Then we will describe the proposed context model and association algorithms.. 2.1 Spatial Context Representation Mathematical notations are given first. The image sequences are denoted as {It: t=1,...,nI}, such that It is the frame at time t. The detected candidates at time t are represented as Ot = { : i = 1, ..., ni} containing ni candidate targets (objects). A

Proc. of SPIE Vol. 8745 87451K-2 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

target o is defined as a vector o = (x(o),h(o),θ(o),a(o)) or simply (x, h, θ, a), where x, h, θ and a are the location, appearance histogram, orientation and area of o respectively.

WAMI video

CAMT Tracker Interested areas, locations, and identifications

Target Target detection tracking Targets of interest, tracklets, tracks

Entity networks formation Network reasoning

Activity analysis

Normalcy Modeling / Site Nomination

Information database

Pattern of life

Ii Figure 1: Diagram of the system extracting pattern of life from WAMI video. At frame t, for a target candidate = (xi, hi, θi,ai) ∈ Ot, its spatial context (SC) is represented by S , which measures the spatial distribution of other candidates in Ot. Specifically, SC divides the neighborhood of into nd×no distanceorientation bins, and S is then defined as a weighted histogram nd×no as (

)

∑

( (

‖

where

)

(

)) ,

(1)

‖

defines the neighborhood of within radius r; ( ( ) ) calculates the relative distance and orientation of with respect to ; =(p∆d, q∆θ) represents the bin(p,q) such that ∆d and ∆θ are the distance and angle interval respectively; ∑ is the estimated covariance matrix; and, Z is the normalization constant. The context-aware multi-target tracker uses the technique of maximum consistency context (MCC), which is a spatiotemporal context and is robust to noises in target neighborhood. With contextual information, MCC effectively reduces the disturbance from neighbor targets which either head in different directions or are false detections. Meanwhile, MCC provides strong discriminative features to guide multi-object association. 2.2 Multi-Object Association Without loss of generality, we assume the two frames to be associated are frames 1 and 2. To handle the missing targets and false detections in O1 and O2, we introduce dummy (or virtual) targets into the two sets and then assume they have the same number of targets, i.e., n1 = n2 = n. The multi-object association can be defined as to find the assignment Π = {πij} ∈{0, 1} n×n to maximize certain total association score, denoted as ɛ(Π; O1, O2). The problem is formulated as: ( s.t.

∑

) ;∑

∑

∑ ;

( ∈{0, 1};

)

(2) i, j ∈{1,...,n},

Proc. of SPIE Vol. 8745 87451K-3 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

(3)

where = 1 (or 0) indicates there is an (or no) association between ; and cij represents the context similarity between and .

and

;

measures the affinity between

and

The association without context modeling can be viewed as a special case where cij= 0, ∀i, j. The association problem turns to a standard integer assignment, where the Hungarian algorithm [19] provides the optimum solution. However, the context modeling here plays a very important role in association. The item sij measures the similarity between targets = (xi,hi,θi,ai) ∈ O1 and appearance, area, and orientation. Specifically, the similarity is defined as

= (xj,hj,θj,aj) ∈ O2, in terms of

sij = αsh,ij+ βso,ij+ (1 − α − β)sa,ij.

(4)

where sh, so and sa denote similarities in appearance, orientation and area, respectively; and α,β are weight factors. To further assess the similarity, we need to determine the consistency. 2.3 Maximum Consistency Context Technique For the two association pairs ( (( where respectively.

( ( ,

) and ( )(

,

), the consistency between them is measured as follows,

))

(

)

(

)

) ( ) ( )) is the orientation and have similar definitions and is the weight parameter.

. (

)

(5) is the and length of (

With this definition, we define the maximum consistency context(MCC) for an association ( ((

) Π) =

(

where ( Π) ( ) ∈ which depends on the association Π.

)∈

(

∈

Π)

((

)(

,

)

) as

))

defines the spatio-temporal neighborhood of (

(6) ),

The proposed MCC is flexible as it does not request the majority consistency in a target’s neighborhood. In contrast to a previous MCC [1], here MCC extracts context information only from the most reliable neighbor association. Such a scheme makes MCC robust to distractions of inconsistent motions in a target’s neighborhood, e.g., the vehicles running in opposite lanes in the highway scenario. The MCC has performed robustly against false detections [1]. ((

To integrate MCC in track association, we define MCC-based association problem Π∑

∑

(

((

) )

( (

)). As a result, we have the following ))).

(7)

To solve the MCC association problem, we use a multi-frame assignment. 2.4 Multiple Frame Association Method As we know, in multi-object tracking, it is very important to associate short reliable tracklets into long tracks, such as track stitching, to maintain track continuity. The reliable tracklet acquisition and tracklet-to-tracklet association, are performed iteratively. Reliable tracklets are obtained by checking the motion smoothness of trajectories. Suppose a basic tracklet is , which is reliable only if the following functions are satisfied

Proc. of SPIE Vol. 8745 87451K-4 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

{

}

.

(8)

where , are the orientation and length of association ( ) respectively; and and are corresponding thresholds. In this way, if association ( ) has inconsistency with adjoining associations, trajectory is divided into two short tracklets (e.g., track splitting) by breaking the association ( ). Tracklet-to-tracklet association follows the similar manner as two-frame multi-target association. First, the affinity between two tracklets is constituted of appearance, motion and temporal similarities. Again, a Hungarian algorithm can be used to associate (e.g., track merging) the two tracklets into the long one. At last, isolated detections and too short tracklets after multi-frame association are discarded as false alarms. The context-aware multi-target tracker provides a solid foundation for the construction of entity networks and POL. To link tracklets, we also need to be aware of interested areas for track localization.

3.

AREA-OF-INTEREST LOCALIZATION

To improve the validity of the our tracking results, we need to know which areas/buildings are of interest, and the correspondence of the interested areas/buildings to the tracking results and terrain maps, for example Google map. It is assumed that some entity (e.g., police) can go to the interested area/building and verify the targets of interest, e.g., a drug dealer. In the WAMI video capturing process, we have the sensor’s location ( ), attitude ( ) ( is the sensor view offset from the Z axis in X-Z plane and is the sensor view offset from the Z axis in Y-Z plane), and field of view (FOV) as shown in Figure 2.Thus, we can locate each point in the image with respect to the sensor. With the knowledge of the position of the sensor in the terrain map, each point in the image can be mapped to the terrain map. Assuming the image has pixels, is the number of pixels in X direction, is the number of pixels in Y direction, , , , . Each pixel in the image occupies angle of in X direction and in Y direction. On the ground (the ground height is ), the pixel with index of ( ) is localized with (

)

(

(

)

(

) ).

(9)

Mapping this point in the reference map, we get the pixel’s location relative the reference/terrain map. But if the pixel corresponds to a building, whose height is not , then we get larger localization error. In WAMI target tracking, most of the interested areas are on the ground, and not points on buildings. Thus this localization technique can be used to localize the interested area for ground plan 2D cooridnates. Assume that there are multiple pixels (e.g.,

pixels) for an interested area, then their locations can be calculated with

∑

,

∑

.

which determines the center of the interested area.

Proc. of SPIE Vol. 8745 87451K-5 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

(10)

Z (𝑥 𝑦 𝑧) FOVy

FOVx

(𝑛 𝑚)

Y

X Figure 2: Interested area localization in WAMI image.

4.

ENTITY NETWORK CONSTRUCTION FROM TARGET TRACKS’ INFORMATION

The overarching purpose of entity network construction is to record the targets’ pattern of life. To record the pattern of life, it is primarily calculated by observing the movement of people or vehicles over a long period, creating network models from historical data, and relating real-time observations with those elements in network models. The core elements of an entity network are the nodes (locations), node attributes (describing a location and activities there), links (tracks between nodes), and link attributes (describing the relationships and trips between nodes, e.g. trips’ duration, direction of trips). For enitity network construction, the first step is to detect the locations of the nodes. Once the entity network nodes are deterimined, the locations can be located on the map database to find its correspondent locations in the terrain. The entity networks are the highest level of activity structure, akin to situation awaerness. Until now, a variety of research has focused on high-level recognition using large sets of tracks’ data [20], group analysis [21], and target group identifications [22]. The limitation is that these results require continous tracks. Constructing a network involves more than the node locations. The task is detecting the nodes, finding links between the nodes, and learning the tracks characteristics. Detection and localization of a node is a basic operation. It is very important to know what spatial areas are possible for a nodes. As the map database is available, we could retrieve the index the location in the database to get the node’s address name, which is one of the node attributes. A node can have varying spatial extant range from small to large (e.g. little building, larger building, compound, neighborhood). Vehicles may park in several specific micro-locations near a node (a two-level node structure), and fine node localization (e.g.building) with the lower track quality, poor track start/end points, building occlusion. Beside nodes, the links between nodes are also one of the networkcore elements. A link between two nodes is a line with direction, indicating the target movement from one node to the other. A vehicle can make any number of short stops on a trip as in move-stop-move tracking. Some nodes are not relevant (benign purpose, stop sign) and some involve unseen people (unseen walk-ride transition, or tracker failure) or less obvious interaction (roll down window to talk with nearby walker or vehicle). The links have some attributes, such as the trajectory start time point, end time point, the duration of the trajectory, and direction of the movement between the nodes. Figure 3 shows an example of network construction based on context-aware multi-target tracker in WAMI for one day.

Proc. of SPIE Vol. 8745 87451K-6 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

F D E G C

H

B

A I Figure 3: An example of network construction.

The nodes are A. living house, B. work place, C. restaurant, D. Supermarket, E. hospital, F. bank, G. supermarket, and H. post office. The links with arrows indicate the target moving from one node to another node. The links without an arrow indicate that the target stayed at the node during the period of observation. Each link has a start time point and an end time point.

5.

RECORDING PATTERN OF LIFE

Based on the tracks of objects and the map information database, we could record the pattern of life (POL) of the objects. From theentity network data, which are recorded in the related database, we extract two kinds of information, time and duration of the activity. An example is shown in the Figure 4. From Figure 1, we assume a path of travel from targets from the Columbus Large Imagery Format (CLIF) data set [23]. Given that targets are moving on roads between destinations, we map to an activity network as in Figure 3. For example, for a track start, node A indicates the living place. The node B is working place. The node D is the supermarket. These nodes are representative of the information from the CLIF data set, with Figure 4 showing an example with semantic labels. With the nodes, we also have link attributes of movments between destinations. Link 1 represents movement from node A to node B, while link 2 is the movement from node B to node A. Link 3 indicates movement from node A to node D. Link 4 is the link from node D to node A. From node B to node D is the link 5, and from the node D to node B is the link 6. The notation continues as nodes are identified. In Figure 5, we see that at the fixed time in six days, most colors in the table are the same,which means that the object has similar life pattern almost everyday. The day begins with staying at the living house for hours, then going to work (as the links represent). Then most of the day time is spent in the work place. After the daytime, the target returns to the livinghouse. The pattern of life is almost repeated every day, except day 6; when the target did not leave the living house.

Proc. of SPIE Vol. 8745 87451K-7 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

li

Figure 4: Labeling of nodes as locations and areas of interest. Node 1

Day 6

Node 2

® Node 3

Day 5

Link 1

Day 4

_ Link 2

Day 3

Link 3

_ Link 4 _ Link 5 _ Link 6

Day 2 Day 1

00:00

2:00

4:00

6:00

8:00

10:00

12:00

14:00

16:00

18:00

20:00

22:00

24:00

Figure 5: Labeling of pattern of life.

6.

CONCLUSION

In this paepr, we presented a technique for extracting pattern of life information from WAMI data. The technique includes context-aware tracking, interested area localization in WAMI images, WAMI correspondence to terrain maps, entity networks construction, and pattern of life recording. We show a promising way to record the pattern of life of interest objects using WAMI data with a time history of events. In the future work, we will enhance the pattern of life extraction with more information, such as more attributes of the entity networks to recognize more behavior of the objects, and visualization of the temporal patterns with the spatial information for situation awareness.

References [1] X. Shi, E. Blasch, W. Hu, and H. Ling, “Using Maximum Consistency Context for Multiple Target Association in Wide Area Traffic Scenes,”Int'l Conf. on Acoustics, Speech and Signal Processing (ICCASP), (2013). [2] C. Zhou, D. Frankowski, P. Ludford, S. Shekhar, and L. Terveen, “Discovering Personal Gazetteers: An Interactive Clustering Approach,” Proc. of ACM GIS, 266-273, (2004). [3] P. Liang, D. Shen, E.P. Blasch, K. Pham, Z. Wang, G. Chen, H. Ling, “Spatial Context for Moving Vehicle Detection in Wide Area Motion Imagery with Multiple Kernel Learning,” Proc. SPIE, 8751 (2013). [4] P. Liang, H. Ling, E. Blasch, G. Seetharaman, D. Shen, and G. Chen, “Vehicle Detection in Wide Aerial Surveillance using Temporal Context,” Int’l Conf. on Information Fusion, (2013). [5] E. Blasch, G. Seetharaman, K. Palaniappan, H. Ling, G. Chen, “Wide-Area Motion Imagery (WAMI) Exploitation Tools for Enhanced Situation Awareness,” IEEE Applied Imagery Pattern Recognition Workshop, (2012). [6] E. Blasch, P. C. G. Costa, K. B. Laskey, H. Ling, G. Chen, “The URREF Ontology for Semantic Wide Area Motion

Proc. of SPIE Vol. 8745 87451K-8 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms

Imagery Exploitation,” IEEE Nat. Aero. and Electronics Conf (NAECON), (2012). [7] P. Liang, T. Teodoro, H. Ling, E. Blasch, G. Chen and L. Bai, “Multiple Kernel Learning for Vehicle Detection in Wide Area Motion Imagery,” Int’l Conf. on Information Fusion, (2012). [8] Y. Wu, G. Chen, E. Blasch, L. Bai and H. Ling, "Feature-based background registration in wide-area motion imagery," Proc. SPIE, 8402, (2012). [9] A.R. Molnar, W.A. Lybrand, L. Hahn, J.L. Kirkman, and P.B. Riddleberger, "Undergrounds in Insurgent, Revolutionary and Resistance Warfare", at http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=AD0436353 [10] T. Mori, R. Urushibata, M. Shimosaka, H. Noguchi, and T. Sata, “Anomaly Detection Algorithm Based on Life Pattern Extraction fromAccumulated Pyroelectric Sensor Data”, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2545-2552 (2008). [11] D. A. Monroe, “Multimedia Surveillance and Monitoring system Including network Configuration”, at http://www.google.com/patents/US6970183 [12] R. Porter, A.M. Fraser, and D. Hush, “Wide-Area Motion Imagery,” IEEE Signal Processing Magazine, 27 (5), 5665 (2010). [13] R. Porter, C. Ruggiero, and J.D. Morrison, “A framework for activity detection in wide-area motion imagery”, Proceedings of the SPIE, 7341, (2009). [14] E. Blasch, C. Banas, M. Paul, B. Bussjager, and G. Seetharaman, “Pattern Activity Clustering and Evaluation (PACE),” Proc. SPIE, Vol. 8402, (2012). [15] R. Rimey, J. Record, D. Keefe, L. Kennedy, and C. Cramer, “Network exploitation using WAMI tracks”, Proceedings of the SPIE 8062 (2011). [16] D. S. Cloud, “CIA drones have broader list of targets,” http://www.latimes.com/news/nationworld/world/la-fgdrone-targets-20100506,0,2689474,full.story [17] A. R. Molnar, “Human Factors Considerations of Undergrounds in Insurgencies” at http://www.soc.mil/ARIS/HumanFactorsS.pdf [18] H. Ling, Y. Wu, E. Blasch, G. Chen, and L. Bai, “Evaluation of Visual Tracking in Extremely Low Frame Rate Wide Area Motion Imagery,” Int. Conf. on Info Fusion, (2011). [19] H.W. Kuhn, “The hungarian method for the assignment problem,” Naval research logistics quarterly, 2 (1-2), 83– 97 (1955). [20] P. Hanselman, C. Lawrence, E. Fortunano, B. Tenney, and E. Blasch, “Dynamic Tactical Targeting,” Proc. of SPIE, Vol. 5441, (2004). [21] E. Blasch and T. Connare, “Improving Track maintenance Through Group Tracking,” Proc of the Workshop on Estimation, Tracking, and Fusion; A Tribute to Yaakov Bar Shalom, Monterey, CA, 360 –371, May (2001). [22] T. Connare, E. Blasch, J. Schmitz, F. Salvatore, and F. Scarpino, “Group IMM tracking utilizing Track and Identification Fusion,” Proc. of the Workshop on Estimation, Tracking, and Fusion; A Tribute to Yaakov Bar Shalom, Monterey, CA, 205 -220, May (2001). [23] O. Mendoza-Schrock, J. A. Patrick, and E. Blasch, ”Video Image Registration Evaluation for a Layered Sensing Environment,” Proc. IEEE Nat. Aerospace Electronics Conf (NAECON), (2009).

Proc. of SPIE Vol. 8745 87451K-9 Downloaded From: http://spiedigitallibrary.org/ on 08/29/2013 Terms of Use: http://spiedl.org/terms