&lt;title&gt;Maximum likelihood method for probabilistic multihypothesis tracking&lt;/title&gt;

Streit, Roy L.; Luginbuhl, Tod

doi:10.1117/12.179066

Cited by 154 publications

(91 citation statements)

References 3 publications

(9 reference statements)

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“…Recently, EM has also been proposed in the target-tracking literature to perform smoothing of tracks, leading to the Probabilistic Multi-Hypothesis Tracker (PMHT) (Avitzour, 1992;Streit & Luginbuhl, 1994;Gauvrit, Le Cadre, & Jauffret, 1997). In the PMHT, the same conditional independence assumptions are made as in this paper, and identical expressions are obtained for the virtual measurements in the M-step.…”

Section: Related Workmentioning

confidence: 74%

“…The concept of using synthetic measurements is not new. It is also used in the tracking literature, where EM is used to perform track smoothing (Avitzour, 1992;Streit & Luginbuhl, 1994).…”

Section: The M-step and Virtual Measurementsmentioning

confidence: 99%

“…Virtual measurements are generated in the E-step, and have two pleasing properties: first, they make it possible to apply off-the-shelf SFM algorithms for learning the model and the camera positions, and second, they are sufficient statistics of the posterior with respect to the problem of learning the model; hence the M-step is mathematically sound. Independently from us, the concept of virtual measurements had already been used in the tracking literature (Avitzour, 1992;Streit & Luginbuhl, 1994).…”

Section: Introductionmentioning

confidence: 99%

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et al. 2003

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Abstract. Learning spatial models from sensor data raises the challenging data association problem of relating model parameters to individual measurements. This paper proposes an EM-based algorithm, which solves the model learning and the data association problem in parallel. The algorithm is developed in the context of the the structure from motion problem, which is the problem of estimating a 3D scene model from a collection of image data. To accommodate the spatial constraints in this domain, we compute virtual measurements as sufficient statistics to be used in the M-step. We develop an efficient Markov chain Monte Carlo sampling method called chain flipping, to calculate these statistics in the E-step. Experimental results show that we can solve hard data association problems when learning models of 3D scenes, and that we can do so efficiently. We conjecture that this approach can be applied to a broad range of model learning problems from sensor data, such as the robot mapping problem.

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Section: Related Workmentioning

confidence: 74%

“…The concept of using synthetic measurements is not new. It is also used in the tracking literature, where EM is used to perform track smoothing (Avitzour, 1992;Streit & Luginbuhl, 1994).…”

Section: The M-step and Virtual Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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et al. 2003

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“…This section concentrates on the techniques proposed in the article [1, 2, and 5]. Authors of [5] proposed probabilistic MHT algorithm to reduce the communication overhead. This technique has an assumption on the association that, they were statistically independent random variables, which eliminates the need for exhaustive enumeration of associations.…”

Section: Point Tracking Techniquementioning

confidence: 99%

An Adaptive Particle Filtering Technique for Tracking of Moving Multiple Objects in a Video

Shrivastava¹,

Nema²

2012

IJCA

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Tracking moving objects in a video is of critical importance in various fields such as traffic monitoring, video surveillance, human motion capture, etc. However, tracking multiple objects in a video is very challenging. To meet that, authors proposed a new adaptive technique for object localization through local and global appearance of target. Local layer concentrates on the target's geometric deformation, i.e. the target structure is updated through adding and removing the local patches. The deformation information is constrained through the global layer, which concentrates on the shape, appearance, and color. The deformation information is passed from local to global layer through particle filter initialization and Hidden Markov Model (HMM). Particle filter is used to detect the local layer patches, and the sequence of deformation information is stored using HMM at global layer. This enhancement to the global layer improves multiple objects tracking efficiency. The efficiency of the proposed technique is evaluated through experimenting with a video containing multiple moving objects. Result analysis shows that the proposed method efficiently tracks multiple moving objects in the video.

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“…Another method for handling data association in the Bayesian manner is the probabilistic multihypothesis tracker (PMHT) [58,59]. PMHT iteratively computes data association probabilities and track updates, using the expectation maximization (EM) method.…”

Section: Bayesian Approachesmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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<title>Maximum likelihood method for probabilistic multihypothesis tracking</title>

Cited by 154 publications

References 3 publications

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An Adaptive Particle Filtering Technique for Tracking of Moving Multiple Objects in a Video

A Survey of Motion-Based Multitarget Tracking Methods

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