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The impact of aspect angle on Doppler effect hinders the capability of a monostatic radar to achieve human activity recognition (HAR) from all aspect angles, i.e., omnidirectional. To alleviate the “angle sensitivity”, sufficient and high-quality training data from multiple aspect angles is mandated. However, it would be time-consuming for the monostatic radar to collect the training data from all aspect angles. To address this issue, this paper proposes a high-quality synthetic data generation algorithm based on high-dimensional model representation (HDMR) for omnidirectional HAR. The aim is to augment a high-quality dataset with collected samples at the radar line-of-sight direction and few samples from other aspect angles. The quality of synthetic samples is evaluated by dynamic time wrapping distance (DTWD) between the synthetic and real samples. Subsequently, the synthetic samples are utilized to train a classifier based on ResNet50 to achieve omnidirectional HAR. Experimental results demonstrate that the averaged HAR accuracy of the proposed algorithm exceeds 91% at different aspect angles. The quality of the synthetic samples generated by the proposed algorithm outperforms two commonly-used algorithms in the literature.
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Generalized Adversarial and Hierarchical Co-occurrence Network based Synthetic Skeleton Generation and Human Identity Recognition
Human skeleton data provides a compact, low noise representation of relative joint locations that may be used in human identity and activity recognition. Hierarchical Co-occurrence Network (HCN) has been used for human activity recognition because of its ability to consider correlation between joints in convolutional operations in the network. HCN shows good identification accuracy but requires a large number of samples to train. Acquisition of this large-scale data can be time consuming and expensive, motivating synthetic skeleton data generation for data augmentation in HCN. We propose a novel method that integrates an Auxiliary Classifier Generative Adversarial Network (AC-GAN) and HCN hybrid framework for Assessment and Augmented Identity Recognition for Skeletons (AAIRS). The proposed AAIRS method performs generation and evaluation of synthetic 3-dimensional motion capture skeleton videos followed by human identity recognition. Synthetic skeleton data produced by the generator component of the AC-GAN is evaluated using an Inception Score-inspired realism metric computed from the HCN classifier outputs. We study the effect of increasing the percentage of synthetic samples in the training set on HCN performance. Before synthetic data augmentation, we achieve 74.49% HCN performance in 10-fold cross validation for 9-class human identification. With a synthetic-real mixture of 50%-50%, we achieve 78.22% mean accuracy, significantly
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- Award ID(s):
- 1950704
- PAR ID:
- 10394185
- Date Published:
- Journal Name:
- 2022 International Joint Conference on Neural Networks (IJCNN)
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IJCNN55064.2022.9892887
