Abstract | ||
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In this paper a new algorithm for multi-targets tracking for roadway environment is proposed. This new approach is based on two parallel tracking stages. Its objective is to improve associations between targets and tracks by avoiding wrong associations which can cause errors on track's path determination. Another interesting point of the proposed approach lies in the fact that the two trackings stages are operated together only when association ambiguities are detected. otherwise, only one tracking is used. This mechanism leads to save computational resources. This contribution comes after previous works achieved at the LIVIC (Laboratory on interactions between vehicles, road network and drivers) regarding to Multi-Hypothesis Tracking (MHT) using the Dempster-Shafer Theory. These previous works discussed the potential interest of considering at the same time multi-hypothesis solutions instead of mono-hypothesis ones. This new approach is more focused on the identification of ambiguities, and runs simultaneously two tracking stages in order to solve these ambiguities thanks to the Dempster-Shafer multi-criteria association rules. The paper will therefore explain quickly the basis of the MHT and then describe the Dual Tracking Ambiguities' Solving (DTAS) algorithm. Finally, a relevant case of study showing the interest of the DTAS will be discussed. |
Year | DOI | Venue |
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2014 | 10.1109/IVS.2014.6856512 | Intelligent Vehicles Symposium |
Keywords | Field | DocType |
data mining,inference mechanisms,intelligent transportation systems,road traffic,target tracking,traffic engineering computing,DTAS algorithm,Dempster-Shafer Theory,LIVIC,MHT,dual multitargets tracking,dual tracking ambiguities,multicriteria association rules,multihypothesis tracking,parallel tracking stage,roadway environment | Data mining,Road traffic,Association rule learning,Artificial intelligence,Intelligent transportation system,Engineering,Dempster–Shafer theory,Trajectory | Conference |
ISSN | Citations | PageRank |
1931-0587 | 2 | 0.53 |
References | Authors | |
2 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Valentin Magnier | 1 | 2 | 0.53 |
Dominique Gruyer | 2 | 485 | 52.30 |