[1]FU Kun,TIAN Jinwen,MA Yichao.Target association from different perspectives based on multi-feature fusion[J].CAAI Transactions on Intelligent Systems,2020,15(5):847-855.[doi:10.11992/tis.202006037]
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Target association from different perspectives based on multi-feature fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 847-855 Column: 学术论文—智能系统 Public date: 2020-09-05
Title:
Target association from different perspectives based on multi-feature fusion
Author(s):
FU Kun1;  TIAN Jinwen1;  MA Yichao2
1. College of Automation, National Key Laboratory of Multispectral Information Processing Technology, Huazhong University of Science and Technology, Wuhan 430074, China;
2. Beijing Electro-mechanical Engineering Institute, Beijing 100074, China
Keywords:
target association; different perspective; cooperative detection; UAV; multi feature fusion; attitude data; topological feature; color feature; D-S evidence theory
CLC:
TP391
DOI:
10.11992/tis.202006037
Abstract:
Target association has great significance in collaborative multi-target detection. As traditional target association algorithms are affected by factors such as changes in view angle, they are less effective when observing targets from different perspectives. This paper proposes a target association algorithm for collaborative reconnaissance by unmanned aerial vehicles (UAVs) based on the fusion of topological and color features. Using position, attitude, and multi-dimensional feature data obtained by the sensors in the UAV, the topological and color features of the target are extracted and the multi-dimensional features are then fused based on the Dempster–Shafer evidence theory to complete the association of target groups from different perspectives. Experiments show that this algorithm framework can effectively help UAVs to complete the task of target group association.
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