A study of clustering applied to multiple target tracking algorithm

Konstantinova, Pavlina; Nikolov, Milen; Semerdjiev, Tzvetan

doi:10.1145/1050330.1050384

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…This is essential in order to acquire velocity rates and augment it to verified-track status. The method, which is similar to data assignment problem discussed in [10], is based on minimizing the normalized distance function (14) that has chi-square 2 M  distribution with M degrees of freedom, resulting in the optimally assigned validation matrix of Fig. 5A.…”

Section: A Measurement Innovation Matrix Is Calculated For Everymentioning

confidence: 99%

Tracking multiple targets from a mobile robot platform using a laser range scanner

Kondaxakis¹,

Kasderidis²,

Trahanias³

2008

IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications

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A major issue in the field of mobile robotics today is the detection and tracking of moving objects (DATMO) from a moving observer. In dynamic and highly populated environments, this problem presents a complex and computationally demanding task. It can be divided in subproblems such as robot's relative motion compensation, feature extraction, measurement clustering, data association and targets' state vector estimation. In this paper we present an innovative approach that addresses all these issues exploiting various probabilistic and deterministic techniques. The algorithm utilizes real laser-scanner data to dynamically extract moving objects from their background environment, using a time-fading grid map method, and tracks the identified targets employing a Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) algorithm. The resulting technique presents a computationally efficient approach to already existing target-tracking research for real time application scenarios.

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Section: A Measurement Innovation Matrix Is Calculated For Everymentioning

confidence: 99%

Tracking multiple targets from a mobile robot platform using a laser range scanner

Kondaxakis¹,

Kasderidis²,

Trahanias³

2008

IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications

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“…Based on these techniques, there are a number of non-Bayesian and Bayesian algorithms for tracking in multiple-target environment [1][2][3][4][5][6][7][8] , e.g., track-splitting approach, nearest neighbourhood (NN) method, maximum likelihood method, joint probabilistic data association approach, multi-hypothesis tracking, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-target Tracking in a Test Range Scenario

Bhattacharya

Raj

2007

DSJ

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Integrated Test Range (ITR) handles various types of multiple-target flight trials. To facilitate target tracking and estimation in such multi-target scenario, nearest neighbourhood (NN) technique-based data association algorithm has been adopted at ITR. The present paper discusses the NN-based data association algorithm and its performance in a real flight trial situation by using multiple-target track data from a multi-target tracking radar.

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