&lt;title&gt;Tracking highly maneuverable targets in clutter using interacting multiple-model fuzzy-logic-based tracker&lt;/title&gt;

Gad, Ahmed S.; Farooq, M.

doi:10.1117/12.477613

Cited by 5 publications

(4 citation statements)

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“…We adopt log interval scale transformation among ordinal scale, interval scale, and log interval scales transformation methods because it is unique in both directions (possibility -3 probability, probability~possibility) while others are not. From (5), the transformation formula of log interval transformation is expressed by (6). addressed in the previous section with our motivational example in this section.…”

Section: Hf3) = N W + W 4x and ) L O G [ P ( X ) L =mentioning

confidence: 99%

“…According to the fomula (l), the degree of concem illustrated in Figure 10 is directly converted (one-to-one) into the associated possibility distribution. The possibility distribution converted from membership function can be converted into the probability distribution through (6). The constant a affects the probability distribution as shown in Table 6.…”

Section: Wo=-~p ( X ) L O G [ P ( X ) Lmentioning

confidence: 99%

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Hybrid model for intent estimation

Lee

LUnas

2003

Sixth International Conference of Information Fusion, 2003. Proceedings of The

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Real-time data acquired 'trnder combat conditions will have different types of imperfection; two representative classes of imperfection are vngueness and ambiguity in the data. A motivational example is created in this paper to explore the prototyping of a hybrid processing model that can formally process both vague fizzy) and ambiguous (probabilistic) data types. The issue is to integrate fhe heterogeneous models, which have dflerent domains. probability and possibiliy. This research provides a hybrid model by exploring and adopting a transformation method between probability andpassibility.identified in Section 2. In Section 4, we introduce the hybrid sofiware prototype utilizing a C H API.

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Section: Hf3) = N W + W 4x and ) L O G [ P ( X ) L =mentioning

confidence: 99%

Section: Wo=-~p ( X ) L O G [ P ( X ) Lmentioning

confidence: 99%

Hybrid model for intent estimation

Lee

LUnas

2003

Sixth International Conference of Information Fusion, 2003. Proceedings of The

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“…Hence, data association module gives a unique label to every tracked object. Many algorithms can be employed to build this module such as Nearest-Neighbor Standard Filter (NNSF) [28], Optimal Bayesian Filter (OBF) [29], Multiple Hypothesis Tracking (MHT) [30], Probabilistic Data Association (PDA) [31], Joint PDA (JPDA) [32], Fuzzy Data Association (FDA) [33], and Viteribi Data Association (VDA) [34]. Although PDA is recommended in cluttered environment, we used JPDA algorithm because it extends PDA to simultaneously track several objects.…”

Section: Server Operationmentioning

confidence: 99%

A Reliable Event-Driven Strategy for Real-Time Multiple Object Tracking Using Static Cameras

Sarhan

Saleh

Elsadek

2011

Advances in Multimedia

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Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly over the last two decades. Researches on such systems still face several theoretical and technical problems that badly impact not only the accuracy of position measurements but also the continuity of tracking. In this paper, a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation and robust tracking system. The proposed tracking strategy is based on scenes captured by a number of static video cameras. Each camera is attached to a workstation that analyzes its stream. All workstations are connected directly to the tracking server, which harmonizes the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a new methodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy. The proposed tracking strategy has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy.

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“…Other feature-aided tracking approaches include the wavelet [14], hypothesis and classification, [10] and vision [17]. Variants of the IMM [9] include the IMM-fuzzy [12], IMM-JBPDAF [6], and MS-IMM [23].…”

Section: Introductionmentioning

confidence: 99%

<title>Modeling intent for a target tracking and identification scenario</title>

Blasch

2004

SPIE Proceedings

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The tracking goal is to reduce positional uncertainty. There are many ways to reduce tracking uncertainty: including classification data, using trafficability maps, and employing behavior information. We seek to extend tracking and identification modeling by incorporating intent to update prediction velocity vectors. A hybrid state space approach is formulated to deal with continuous-valued kinematics and discrete-valued target type, pose (inherently continuous but quantized), and intent behavior. The coupled tracker design is illustrated within the context of using ground moving target indicator (GMTI) and high range-resolution (HRRR) measurements as well as digital terrain elevation data (DTED), road map, and estimated goal states. The resulting Intent Coupled Tracking and Identification (ICTI) system is expected to outperform separately designed systems particularly during target maneuvers and recovering from temporary data dropout.

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<title>Tracking highly maneuverable targets in clutter using interacting multiple-model fuzzy-logic-based tracker</title>

Cited by 5 publications

References 0 publications

Hybrid model for intent estimation

Hybrid model for intent estimation

A Reliable Event-Driven Strategy for Real-Time Multiple Object Tracking Using Static Cameras

<title>Modeling intent for a target tracking and identification scenario</title>

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