Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition

• URL: http://arxiv.org/abs/2211.04331v1
• Date: Tue, 8 Nov 2022 15:48:44 GMT
• Title: Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition
• Authors: Hyeongju Choi, Apoorva Beedu, Harish Haresamudram, Irfan Essa
• Abstract summary: We propose a multi-modal framework that learns to effectively combine features from RGB Video and IMU sensors. Our model is trained in two-stage, where in the first stage, each input encoder learns to effectively extract features. We show significant improvements of 22% and 11% compared to video only, and 20% and 12% on MMAct datasets.
• Score: 6.0306313759213275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To properly assist humans in their needs, human activity recognition (HAR) systems need the ability to fuse information from multiple modalities. Our hypothesis is that multimodal sensors, visual and non-visual tend to provide complementary information, addressing the limitations of other modalities. In this work, we propose a multi-modal framework that learns to effectively combine features from RGB Video and IMU sensors, and show its robustness for MMAct and UTD-MHAD datasets. Our model is trained in two-stage, where in the first stage, each input encoder learns to effectively extract features, and in the second stage, learns to combine these individual features. We show significant improvements of 22% and 11% compared to video only and IMU only setup on UTD-MHAD dataset, and 20% and 12% on MMAct datasets. Through extensive experimentation, we show the robustness of our model on zero shot setting, and limited annotated data setting. We further compare with state-of-the-art methods that use more input modalities and show that our method outperforms significantly on the more difficult MMact dataset, and performs comparably in UTD-MHAD dataset.

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