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- W2897122831 abstract "Visual aided guide system (VaGS) is essential and important for the future UAVs, also for the mixed ground command and guide of both UAVs and manned fighters. The key module of VaGS is to recognize the commander‘s gestures and translate them into instructions. This paper introduces a new high-performance gesture recognition architecture for VaGS. It includes two main components: (1) a multiscale structure is adopted to conduct feature learning, which combined global human gesture features with local hands gesture features rather than an extremely deep neural networks; (2) the fused features are input into an extreme learning machine (ELM) for a classification procedure and then output the gesture instructions. Experimental results show that the proposed method can achieve a rather better expression in some standard datasets, and obtain an accuracy of 99.6% in an aviation guide gesture dataset with 40 classes built by ourselves, still a less training and forward time consumption." @default.
- W2897122831 created "2018-10-26" @default.
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- W2897122831 date "2018-10-17" @default.
- W2897122831 modified "2023-09-27" @default.
- W2897122831 title "Aviation Guide Gesture Recognition Using ELM with Multiscale CNN Features" @default.
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- W2897122831 doi "https://doi.org/10.1007/978-3-030-01520-6_23" @default.
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