Summary of Tracking and Identification Methods

Summary of Tracking and Identification Methods Erik Blasch1, Chun Yang 2, Ivan Kadar 3 1

Air Force Research Laboratory, Information Directorate, Rome, NY, 13441 2 Sigtem Technology, Inc., 1343 Parrott Drive, San Mateo, CA 94402 3 Interlink Systems Sciences, Inc., Lake Success, NY 11042

ABSTRACT Over the last two decades, many solutions have arisen to combine target tracking estimation with classification methods. Target tracking includes developments from linear to non-linear and Gaussian to non-Gaussian processing. Pattern recognition includes detection, classification, recognition, and identification methods. Integrating tracking and pattern recognition has resulted in numerous approaches and this paper seeks to organize the various approaches. We discuss the terminology so as to have a common framework for various standards such as the NATO STANAG 4162 - Identification Data Combining Process. In a use case, we provide a comparative example highlighting that location information (as an example) with additional mission objectives from geographical, human, social, cultural, and behavioral modeling is needed to determine identification as classification alone does not allow determining identification or intent. Keywords: Belief Functions, Classification, Recognition, Identification, Tracking, NATO STANAG 4162, DSmT, PCR5, Simultaneous Tracking and Identification

1. INTRODUCTION Over the last couple of decades, there has been significant work in tracking and detection, tracking and classification, and tracking and identification. For each of these methods, there is a difference in the target acknowledgement to include measurements, attributes, and allegiance; respectively. For classification and identification, it is the labeling of the target that is a key component. Benefits of track labeling include target recognition [1], increasing track life [2], and providing more rich information for the user [3,4,5]. One of the challenging issues for simultaneous tracking and identification is the different uses of the terminology. Thus, this paper seeks to review some of the literature and more carefully defining tracking and identification. Figure 1 highlights the use of the terms: detection, classification, recognition, and identification that are standard across academic, industry, and government discussions versus individual author presentations. Level 0 Data Assessment • Get the object location, preprocess Detect

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Level 4 - Process Refinement • Other Sensors - Behavior • Intelligence Information

Level 5 - User Refinement • Modify/ Select Sensor Placements • Determine information needs • Choose algorithms, target priority

Figure 1: Target tracking as related to the Information Fusion Process levels [6]. There are three assumptions of the analysis to include: (1) the NATO STANAG 4162, as well as other international Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, edited by Ivan Kadar, Proc. of SPIE Vol. 9091, 909104 · © 2014 SPIE · CCC code: 0277-786X/14/$18 · doi: 10.1117/12.2050260

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standards, provides a unifying definition of what is meant by “identification” in a tracking context; (2) “identification” comes from a variety of sensing sources such as radar, electro-optical systems, and semantic descriptions, which has some consistency in definitions for multiple decades, and (3) random references to identification, when the author meant classification, are discounted. The misuse of the terminology “identification,” when the author meant identification is reasonable if a researcher was not focused on the semantic description of their methods and traditionally, the discrimination between classification/identification was not well defined. Hence, this paper seeks to revisit the discussion so as to provide a discussion on the research in tracking and identification. Simultaneous tracking and identification (STID) method is a portion of the Data Fusion Information Group model [7, 8, 9, 10], which organizes the processes of information fusion into various stages. Level 0 (registration), Level 1 (object assessment), Level 2 (situation assessment), and Level 3 (threat assessment) include the estimation techniques. The control functions include: Level 4 (process refinement), Level 5 (user refinement), and Level 6 fusion (mission management) provides feedback methods towards the collection of measurement data [11]. There are three versions of tracking techniques: Level 1: Tracking and Detection Tracking and Classification (to include feature-aided tracking) Level 2: Tracking and Object-association (to include track group) Level 3: Tracking and Identification

Numerous textbooks [12, 13, 14, 15, 16, 17] and background papers on target tracking techniques have been reported [18, 19, 20, 21]. The discussion on tracking and recognition/classification/identification needs has been a subject of many panel discussions [22, 23] from which various authors have provided differing approaches and techniques towards solving the issues. In 2006 [24], the link between object assessment tracking and situation assessment was discussed which focused on knowledge representation. In 2007 [25, 26], the implications of STID were discussed in relation to threat assessment and sensor management. In 2008, tracking performance analysis [27] was highlighted with ATR sensor management [28]. Finally, other panels have discussed tracking and identification robustness [29], combination with text labeling [30], and social-cultural behavioral modeling [31], and big data analysis. The paper is organized as follows: in Section 2, we bring together many definitions of classification, recognition, and identification towards a common use of the terms. Section 3 discusses the STANAG 4162: Identification Data Combining Process. Section 4 is a literature review and Section 5 discusses a systems approach to tracking and identification. An example is shown in Section 6, metrics in Section 7, and finally conclusions in Section 8.

2. CLASSIFICATION, RECOGNITION, AND IDENTIFICATION DEFINITIONS The definitions of classification, recognition, and identification are slightly different based on the use of the terms in different communities. Here, we overview these definitions in the hope to solidify an understanding for the community. There are many communities using the terms in a similar way, but mean different things such as biology, engineering, pattern recognition, and the military. To start, we begin with the dictionary. 2.1 Dictionary definitions of Classification, Recognition, and Identification Classification is defined as (1) the act or process of putting people or things into groups based on ways that they are alike, or (2) an arrangement of people or things into groups based on ways that they are alike. Classifying is thus a systematic arrangement in groups or categories according to established criteria (e.g., taxonomy). The categories could be object behaviors (e.g., movement), appearances (e.g., size), and affiliations (e.g., group). In many cases, the determination of the information that supports a class discussion comes from signatures (1D, 2D, 3D), features, and combined attributes. Recognition is (1) the act of accepting that something is true or important or that it exists, or (2) the act of knowing who or what someone or something is because of previous knowledge or experience. Recognizing is an acknowledgement or feeling that something or someone is present alluding to a special notice or attention from the sensing and encoding by a machine. Given a structured approach to many man-made systems, examples include: optical character recognition, automatic target recognition, or face recognition. What is common about these definitions is that there is some defined template, model, or set of features that have been trained and linked to an object type (such as printed letters or

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2.3 Military definitions of Classification, Recognition, and Identification For classification, a standard is the National Imagery Interpretability Rating Scale (NIIRS) that is also used for civilian applications. The NIIRS is based on the Johnson Criteria1 [35]. Johnson’s criteria determine the minimum resolution in terms of line pairs across a target that put the target into these categories as the resolution amount gives a 50 percent probability for an observer (or machine) to discriminate an object to the specified level:    

Detection, an object is present (1.0 +/– 0.25 line pairs) Orientation, symmetrical, asymmetric, horizontal, or vertical (1.4 +/– 0.35 line pairs) Recognition, the type object can be discerned, a person versus a car (4 +/– 0.8 line pairs) Identification, a specific object can be discerned, a woman versus a man, the specific car (6.4 +/– 1.5 line pairs)

From these definitions, established in 1948, classification was not noted as image resolution does not imply things were put into classes. However, two systems emerged and became known as Automatic Target Recognition (ATR) and Combat Identification (CID). ATR looks at an object type such as a tank. On the other hand, CID is based on the Identify Friend, Foe, or Neutral (IFFN) paradigm for cooperative ID. As above, a unique signature in communications can give a signal as to the exact system. However, with non-cooperative ID, then multiple sensors are needed to confirm the exact entity being observed. The sensors are used to then identify the entity based on using measurements to detect features to recognize which class it belongs. The problem is that recognition narrows the choices into a subset of options of information within a class. From there, other information such as allegiance, behaviors, location, and cultural (e.g., political) information is needed for further disambiguation of entity within the class. More specifically, there is a need for standard which is being defined by the STANAG 4162: Identification Data Combining Process.

3. STANAG 4162: IDENTIFICATION DATA COMBINING PROCESS STANAG 4162 understands that identification of an object and the assessment of its affiliation and threat potential are essential capabilities in civil and military surveillance systems [36]. The standard supports interoperability through consistent and reliable processing, scalable to any kind of sensor modality, flexible for information management and distributed processing, and supports the operators through workload reduction. The Identification Data Combing Process (IDCP) approach uses a Naïve-Bayes analysis in three stages:  Characterization of Identification Interest: The IDCP approach models mission-based operational identification and discrimination aspects from classes: Friend, Assumed Friend, Neutral, etc., with operational attributes such as the allegiances Own, Neutral, and Enemy Forces as example.  Description of ID Sources: Sources include the real and modeled uncertainties and inaccuracies resulting from the measurements using conditional probabilities. Technical attributes could include radar frequency type, imaging resolution, and entity adherence to a requested heading change.  Operational Interpretation of Source Information: Linking the operational interpretation of source information to technical attributes accounts for uncertainties of the transformation processing. Again, the interpretation is described by means of conditional probabilities such as valid IFF modes, image resolution, or standard/structured communication.

Current techniques being proposed for IDCP include a Bayesian formulation in which the ID has to be determined from a set of possibilities narrowed by a classification as non-friendly. Kruger and Ziegler [37] have formulated the problem through understanding the source data (user, platform and sensor pedigree, and target behavior). Future sources would include environmental and cultural factors. Assuming IFFN capabilities, they address the non-cooperative ID scenario for an airport analysis divided into the following classes, summarized below (with the associated a prior use of IFFN communications):

Military Civilian

1

IFFN 90% 10%

Own No IFFN 1% 99%

IFFN 50% 50%

Natural No IFFN 1% 99%

http://en.wikipedia.org/wiki/Johnson's_criteria

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IFFN 10% 90%

Hostile No IFFN 1% 99%

2.3 Military definitions of Classification, Recognition, and Identification For classification, a standard is the National Imagery Interpretability Rating Scale (NIIRS) that is also used for civilian applications. The NIIRS is based on the Johnson Criteria1 [35]. Johnson’s criteria determine the minimum resolution in terms of line pairs across a target that put the target into these categories as the resolution amount gives a 50 percent probability for an observer (or machine) to discriminate an object to the specified level:    

Detection, an object is present (1.0 +/– 0.25 line pairs) Orientation, symmetrical, asymmetric, horizontal, or vertical (1.4 +/– 0.35 line pairs) Recognition, the type object can be discerned, a person versus a car (4 +/– 0.8 line pairs) Identification, a specific object can be discerned, a woman versus a man, the specific car (6.4 +/– 1.5 line pairs)

From these definitions, established in 1948, classification was not noted as image resolution does not imply things were put into classes. However, two systems emerged and became known as Automatic Target Recognition (ATR) and Combat Identification (CID). ATR looks at an object type such as a tank. On the other hand, CID is based on the Identify Friend, Foe, or Neutral (IFFN) paradigm for cooperative ID. As above, a unique signature in communications can give a signal as to the exact system. However, with non-cooperative ID, then multiple sensors are needed to confirm the exact entity being observed. The sensors are used to then identify the entity based on using measurements to detect features to recognize which class it belongs. The problem is that recognition narrows the choices into a subset of options of information within a class. From there, other information such as allegiance, behaviors, location, and cultural (e.g., political) information is needed for further disambiguation of entity within the class. More specifically, there is a need for standard which is being defined by the STANAG 4162: Identification Data Combining Process.

3. STANAG 4162: IDENTIFICATION DATA COMBINING PROCESS STANAG 4162 understands that identification of an object and the assessment of its affiliation and threat potential are essential capabilities in civil and military surveillance systems [36]. The standard supports interoperability through consistent and reliable processing, scalable to any kind of sensor modality, flexible for information management and distributed processing, and supports the operators through workload reduction. The Identification Data Combing Process (IDCP) approach uses a Naïve-Bayes analysis in three stages:  Characterization of Identification Interest: The IDCP approach models mission-based operational identification and discrimination aspects from classes: Friend, Assumed Friend, Neutral, etc., with operational attributes such as the allegiances Own, Neutral, and Enemy Forces as example.  Description of ID Sources: Sources include the real and modeled uncertainties and inaccuracies resulting from the measurements using conditional probabilities. Technical attributes could include radar frequency type, imaging resolution, and entity adherence to a requested heading change.  Operational Interpretation of Source Information: Linking the operational interpretation of source information to technical attributes accounts for uncertainties of the transformation processing. Again, the interpretation is described by means of conditional probabilities such as valid IFF modes, image resolution, or standard/structured communication.

Current techniques being proposed for IDCP include a Bayesian formulation in which the ID has to be determined from a set of possibilities narrowed by a classification as non-friendly. Kruger and Ziegler [37] have formulated the problem through understanding the source data (user, platform and sensor pedigree, and target behavior). Future sources would include environmental and cultural factors. Assuming IFFN capabilities, they address the non-cooperative ID scenario for an airport analysis divided into the following classes, summarized below (with the associated a prior use of IFFN communications):

Military Civilian

1

IFFN 90% 10%

Own No IFFN 1% 99%

IFFN 50% 50%

Natural No IFFN 1% 99%

http://en.wikipedia.org/wiki/Johnson's_criteria

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IFFN 10% 90%

Hostile No IFFN 1% 99%

The key note is that IFFN is only available for own-ship analysis with bonus communication for neutral actors. Essentially, if the ID is not available, then there is a need to follow through the detection, classification, recognition, and identification paradigm with advanced attributes of kinematic and appearance information from multi-intelligence sources. Kruger and Ziegler [38] improved on their Bayesian model with Bayesian ID network that puts into classes many types of maritime vessel affiliations. Additional efforts could include the path and behavior of travel to distinguish the objects [39]. What is needed then is further analysis from tracking information to afford identification.

4. TACKING AND IDENTIFICATION RESEARCH Over the years, there have been many proposed methods in tracking and identification. Figure 3 showcases these elements of which we are concerned with the Level 1 processing for tracking and identification. It is noted that the ICDP is focused on the link between Level 1 and Level 3 processing of identification leading towards impact assessment. "-DATABASE SUPPORT

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Figure 3: Information Fusion Functions. 4.1 Tracking and Identification Having sorted and clarified the definitions of tracking and identification, this literature review looks at key papers as well as those that addressed the topic of simultaneous tracking and identification. The key developments in the 80’s began with numerous discussions on radar development and the various designs for identification [40]. A seminal paper by Mori, Chong, and Wisher in 1986 [41] characterized the classification versus identification issue by understanding that without identification information (e.g., IFFN labels), there was a need to detect and classify targets. Researchers in the early 90’s looked at Bayesian approaches to solving the problem from the available sensors (e.g., video and radar). Durrant-Whyte et al. [42] used the terminology similar to and with ambiguity as the Johnson criteria and developed a decentralized approach for tracking and identification by detecting targets with multiple sensors; however, there were no implications of the target allegiance, but only labels for the target type (aka. classification). Likewise O’Sullivan and Jacobs [43] used the terminology by tracking and recognizing the target type from high-range resolution radar (HRR) profiles. No discussion is made of classification from which one could potentially infer target allegiance. Other methods were used to classify tracks [44]. In 1997, others while implying identification, used the classification for target track discrimination analysis in clutter [45] and target determination [46, 47, 48]. These works used a confusion matrix which assumed some apriori training to start with a sensor-based classification update to the target track state. In implied analysis, the target’s classification is related to allegiance, then identification can be attributed to the target recognized from kinematic behavior. Using the ability to perform tracking and classification, multiple sensors could be used in the analysis to determine the target identification [49, 50]. By having knowledge of the apriori training of sensor measurements, a classifier could be trained on a specific set or target classes such that knowing the exact target type and location could infer the allegiance and identity of the target (e.g., IFFN) [51]. For example, with detailed analysis of HRR profiles, targets aligned to known target types with specific behaviors could be discriminated between sets of friend, foe, and neutral targets [52, 53, 54] which was reported as simultaneous tracking

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and identification (STID). Another approach was to use the results from Electronic Support Measures (ESM) sensors for the target classifier to update a tracker [55, 56]. The use of tracking, classification, and identification has many advantages. By knowing the identity of a target, tracking could be updated to improve track maintenance [57, 58]. Likewise, identification information supports sensor management [59] and group tracking [60, 61]. Furthermore, since most STID results are presented to an operator, the user’s could refine the identification estimate based on context [62]. 4.2 Tracking and Identification using Radar The classification information can aid target tracking. As a matter of fact, with multiple sensor modes such as Synthetic Aperture Radar (SAR) for stationary targets and HRR for moving targets, the STID methods were applied to moving and stationary target analysis [63]. The coupling with IFFN sensors improved the kinematic state [64, 65, 66]. Using the features, attributes, and categories supports mutual aided STID [67, 68]. Another approach included using Ground Moving Target Indicator (GMTI) [69] data for maneuvering targets and the HRR analysis to get ID when the target was moving with enough constancy so as to classify and identify the target [70, 71, 72, 73, 74]. Simulated approaches include the Probability Multi-Hypothesis Tracker (PMHT) [75], interactive multiple modeling (IMM) [76, 77], evidential reasoning [1, 78, 79, 80], Particle Filter (PF) [81], Unscented Kalman Filter (UKF) [82], feature matching [83], and mean-shift classifier [84]. Together, a system could determine intent [6]. 4.3 Tracking and Identification with EO/IR systems Classification of targets can come from multiple looks of 1D information such as radar or from different sensors such as imaging. Electro-optical/infrared (EO/IR) has complementary information in that a target could be classified and associated with IFFN sensors. EO/IR tracking and identification includes air-to-ground targeting [85], video security [86], people tacking with heat signatures [87] and biometrics [88, 89], and chemicals in clouds [90]. Multi-object tracking and identification includes vehicles [91], pedestrian gait [92], and leaders in groups [93]. The computer vision community has numerous challenge problems to detect, track, classify and determine the behavior/intent of entities [94]. Other methods include using fusing the EO/IR imagery [95] and then doing tracking and identification with Short-Wave Infrared (SWIR) [96] or Polarimetric imaging [97]. 4.4 Tracking and Identification for Ballistic Target Tracking Ballistic target tracking inherently is tracking and identification as the action is assumed to be threatening. The missile itself leads to the identification from its detected location [98, 99, 100].

5. TRACKING AND IDENTIFICATION PROCESS 5.1 Tracking and Identification for Evaluation An important component to tracking and identification design is evaluation. We highlighted a design of experiments approach to testing tracking and identification systems [101], but there is also a need to consider the systems-level aspects of analysis such as the tracking method [102, 103, 104], metrics [105, 106], use of classification information [107, 108], and performance improvement for tracking through target movement [109] and sensor management [110]. 5.2 Tracking and Identification System The future of STID comes from the various features, attributes, and classifications combined with kinematics estimates that lead to intent. Key systems enablers include human computer interfaces (HCIs) that allows a user to confirm, link, or associate this information to identify an object. Additional supporting information is needed from various models (sensor, environment, and target) to aid real-time STID, as shown in Figure 4. The STID supporting information includes data management, user interaction, and models for enhanced analysis. Data management was discussed at the SPIE 2013 panel for big data analytics of which methods are using cloud computing [111]. User interaction has also been featured as a subject of High-Level Information Fusion [112]. The internal STID modeling includes kinematic models updates [113], target model refinement [114], and resource management [115]. The external STID modeling includes road tracking [116], occlusion detections [117], external signals for referencing [118], and activity analysis [119].

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Tracking And ^Massification

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Figure 4: System Architectures.

6. EXAMPLE OF TRACKING AND IDENTIFICATION From the previous applications where we use various kinematic tracking (Bayes, Dempster-Shafer, and PCR5) [1, 120, 121] and classification methods [122,123,124], we highlight the use of classification for identification. Assuming that we have made a classification of an entity, which we know is a vehicle but have no communication with, STID has to determine whether the vehicle is friendly or hostile. Something could be a hostile if it is in the wrong location, moving at suspicious speeds, and/or has hostile positioning. In this case, we have a classification of a vehicle, but are unsure of its intent. For our scenario, the object is on a road and its designation is neutral or potentially hostile, shown in Figure 5a. We assume that if the vehicle is friendly, we have cooperative ID. However, with no communication, we have to perform identification from the available information to determine if hostile or neutral. The IFFN classification is CM=[0.7 0.3; 0.3 0.7] due to the inability to correctly classify the targets intent and we use the Joint Belief Probability Data Association (JBPDAF) tracker [1] with Probability Conflict Redistribution 5 (PCR5). The analysis shown in Figure 6, which compares the Bayesian approach with the PCR5 approach that highlights the changing identification (hostile), requires advances methods for inclusion in the STANAG 4162. From Figure 5b, the area designated in red (after joint tracking and classification) is the vehicle position near a restricted area (such as in front of a building). target track and ID

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Figure 5: Simulation: (a) Object is on a road and (b) results after identification where red is hostile and green is neutral.

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Figure 6: Analysis showing the Identification.

7. DISCUSSION STID methods require additional metrics which extend Measures of Performance (MOPs) towards Measures of Effectiveness (MOEs) [125]. Below, we highlight the needs for additional metrics as related to the STANAG 2511 for reliability and credibility classification [126] and STANAG 4067 for tracking. Metrics Assessment Level 1

Data Object MOP

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Situation Threat Process User MOE

Level 6

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Track Relevant Reliability Credibility Accuracy Patterns Intent

Identification Provenance Pedigree Detection Recognition Classification Activity Score Identification

Covariance Timeliness Computation Confidence Efficient

Conflict Attention Workload Trust Effective

8. CONCLUSIONS In this paper, we summarized the methods in tracking and identification by conducting a literature review and highlighting the developments in the last two decades. The paper highlighted the need for a common terminology so as to support interpretability between systems sharing detection, classification, and identification information from tracking systems. The various NATO STANAGs as a common standard is needed for consistency in terminology and developments. As a use case example, we simulated the effects of a Bayesian and Belief-based tracking to show that determining the identification (friend, foe, natural) of a hostile moving entity requires information on the location as classification alone does not determine the identification or intent.

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