Generalized Murty&amp;#x0027;s algorithm with application to multiple hypothesis tracking

Fortunato, Eduardo; Kreamer, William; Mori, Shintaro; Chong, Chee-Yee; Castanon, Greg

doi:10.1109/icif.2007.4408017

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…networks [35], we separately compare PVOC with BIGCLAM (which is scalable and also the most competing algorithm) on actual large real datasets. Many optimization algorithms have the tendency to underestimate smaller size communities [50] and sometimes tend to produce very large size communities. In our test suite, we observe the similar tendency in BIGCLAM whereas the communities obtained by PVOC based algorithms are comparable in size with respect to the groundtruth.…”

Section: Resultsmentioning

confidence: 99%

Leveraging disjoint communities for detecting overlapping community structure

Chakraborty¹

2015

J. Stat. Mech.

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Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network.In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure.We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.

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Section: Resultsmentioning

confidence: 99%

Leveraging disjoint communities for detecting overlapping community structure

Chakraborty¹

2015

J. Stat. Mech.

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“…With this algorithm, we can select the k new global hypotheses with highest weight for a given global hypothesis j without evaluating all the newly generated global hypotheses [17], [21], [30], [31]. An interesting alternative would be to use the generalised Murty's algorithm for multiple frames [32].…”

Section: Updatementioning

confidence: 99%

Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation

García-Fernández

Williams

Granström

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

221

251

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We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multi-target tracking with the standard point target measurements without using probability generating functionals or functional derivatives. We also establish the connection with the δ-generalised labelled multi-Bernoulli (δ-GLMB) filter, showing that a δ-GLMB density represents a multi-Bernoulli mixture with labelled targets so it can be seen as a special case of PMBM. In addition, we propose an implementation for linear/Gaussian dynamic and measurement models and how to efficiently obtain typical estimators in the literature from the PMBM. The PMBM filter is shown to outperform other filters in the literature in a challenging scenario.

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“…Proses tracking dengan metode MHT dan menggunakan proses kluster untuk pengelompokan data setiap langkah lintasan robot berdasarkan waktu. Pada proses algoritma MHT, terdapat langkah proses penentuan hipotesis dimana pada tahapan ini ada proses penentuan nilai track score atau nilai probabilitas yang cukup optimal yaitu algoritma Murty [7] dan [8] yang memang sering digunakan di dalam metode MHT. Pengelompokan data pada proses MHT juga pernah dilakukan oleh Makris dkk [9].…”

Section: Pendahuluanunclassified

Multitarget Tracking Menggunakan Multiple Hypothesis Tracking Dengan Clustering Time Window Data Radar

Cahyono¹,

Tjandrasa²

2015

JUTI

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ABSTRAK ABSTRACTThe radar systems are categorized into two types: civilian and military radar systems. The two types of radar systems have similarity that has been used for monitoring air traffic. The aircraft that is monitored in the air is currently experiencing a large amount improvement. To facilitate the aircraft monitoring, it required a system called multitarget aircraft tracking. This research proposed a hybrid method of multitarget tracking algorithm (MTT) with time window clustering for data pre processing. This research started with the preparation of the radar data recorded directly. Data recording was simulated with algorithms that have been designed. Finally, performance of the algorithm was compared with multiple hypothesis tracking without clustering time window. The test is done using data recording lasted approximately eighteen minutes. The test result shows that the proposed method is better than the MHT without CTW method. It can be seen from the correct target 87.66%, the undetected target 12.81%, the maintain target 80.5% and the inexisting target 23.65%.

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Generalized Murty's algorithm with application to multiple hypothesis tracking

Cited by 14 publications

References 14 publications

Leveraging disjoint communities for detecting overlapping community structure

Leveraging disjoint communities for detecting overlapping community structure

Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation

Multitarget Tracking Menggunakan Multiple Hypothesis Tracking Dengan Clustering Time Window Data Radar

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