Tracking the Splitting and Combination of Group Target With $\delta$ -Generalized Labeled Multi-Bernoulli Filter

Multiple resolvable group target estimation using graph theory and the multi-Bernoulli filter

2016 International Conference on Control, Automation and Information Sciences (ICCAIS) ◽

10.1109/iccais.2016.7822429 ◽

2016 ◽

Author(s):

Weifeng Liu ◽

Shujun Zhu ◽

Chenglin Wen

Keyword(s):

Graph Theory ◽

Bernoulli Filter ◽

Group Target ◽

Resolvable Group

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Tracking Split Group with δ-Generalized Labeled Multi-Bernoulli Filter

Journal of Sensors ◽

10.1155/2019/9278725 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Linhai Gan ◽

Gang Wang

Keyword(s):

Performance Evaluation ◽

Initial Development ◽

New Targets ◽

Target Splitting ◽

Bernoulli Filter ◽

Group Target

As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a δ-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict the splitting events of all the GGIW components in every iteration. With component combination applied in adaptive birth, a redundant modeling for a newborn target or preexisting target could be avoided. Moreover, a method for labeling performance evaluation of the algorithm is provided. Simulations demonstrate the effectiveness of the proposed approach.

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Social attribution, correspondence bias, and the emergence of stereotypes *This research has been completed in partial requirement of a doctoral dissertation of the first author under the supervision of the second author. We wish to thank the members of the social psychology division at the University of Louvain at Louvain-la-Neuve, especially Steve Rocher and Emanuele Castano for their insightful comments on preliminary drafts. This paper has been enriched by feedbacks received from Russell Spears, Roos Vonk, Margit Oswald, and anonymous reviewers. Many thanks to Philippe Kemp and Christophe Remy for their assistance in running the study, and to the students who volunteered to appear on the video. This research has been presented at the Social Cognition workshop of the Kurt Lewin Institute, workshop held at the Free University of Amsterdam in April 1998: we want to thank participants of this workshop and especially Gün Semin for their useful feedback. Completion of this study benefited from a research grant from the Belgian National Fund for Scientific Research to the first author.

Swiss Journal of Psychology ◽

10.1024//1421-0185.58.4.233 ◽

1999 ◽

Vol 58 (4) ◽

pp. 233-240 ◽

Cited By ~ 13

Author(s):

Anouk Rogier ◽

Vincent Yzerbyt

Keyword(s):

Correspondence Bias ◽

Social Attribution ◽

The Social ◽

Dispositional Attributions ◽

Kurt Lewin ◽

Group Members ◽

Group Target ◽

The University ◽

Institute Workshop ◽

Research Grant

Yzerbyt, Rogier and Fiske (1998) argued that perceivers confronted with a group high in entitativity (i.e., a group perceived as an entity, a tight-knit group) more readily call upon an underlying essence to explain people's behavior than perceivers confronted with an aggregate. Their study showed that group entitativity promoted dispositional attributions for the behavior of group members. Moreover, stereotypes emerged when people faced entitative groups. In this study, we replicate and extend these results by providing further evidence that the process of social attribution is responsible for the emergence of stereotypes. We use the attitude attribution paradigm ( Jones & Harris, 1967 ) and show that the correspondence bias is stronger for an entitative group target than for an aggregate. Besides, several dependent measures indicate that the target's group membership stands as a plausible causal factor to account for members' behavior, a process we call Social Attribution. Implications for current theories of stereotyping are discussed.

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