Semisupervised Radar-Based Gait Recognition in the Wild via Ensemble Determination Strategy

Xu, Jingxuan; Hou, Yonghong; Yang, Yang; Li, Beichen; Wang, Qing; Lang, Yue

doi:10.1109/jsen.2022.3208737

Cited by 2 publications

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“…One flaw of this approach is the need for many data as well as significant calculating power. The other approach is the application of a group of simple classifiers which share in determining the classifying decision [ 19 , 20 , 21 ]. The contrast with deep learning classifier ensembles, in general, consists of simple classifiers with less enhanced algorithms.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Derlatka

Borowska

2023

Sensors

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Human gait recognition is one of the most interesting issues within the subject of behavioral biometrics. The most significant problems connected with the practical application of biometric systems include their accuracy as well as the speed at which they operate, understood both as the time needed to recognize a particular person as well as the time necessary to create and train a biometric system. The present study made use of an ensemble of heterogeneous base classifiers to address these issues. A Heterogeneous ensemble is a group of classification models trained using various algorithms and combined to output an effective recognition A group of parameters identified on the basis of ground reaction forces was accepted as input signals. The proposed solution was tested on a sample of 322 people (5980 gait cycles). Results concerning the accuracy of recognition (meaning the Correct Classification Rate quality at 99.65%), as well as operation time (meaning the time of model construction at <12.5 min and the time needed to recognize a person at <0.1 s), should be considered as very good and exceed in quality other methods so far described in the literature.

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Section: Introductionmentioning

confidence: 99%

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Derlatka

Borowska

2023

Sensors

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“…Among existing radar sensing research, several studies have exploited semi-supervised learning techniques to reduce the dependence on labeled samples. For example, [47] proposes a SSL training strategy for gait recognition. Due to the various types of interference in data collection, caused by for example different weather or environmental factors, unlabeled and labeled data may exhibit different data distributions.…”

Section: B Semi-supervised Learning For Radarmentioning

confidence: 99%

ARC: Automotive Radar Consistency Regularization for Semi-Supervised Learning

Lee,

Jovanov,

Kumcu

et al. 2024

IEEE Trans. Intell. Veh.

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In recent years, radar has become a crucial part of road scene perception. In particular, radar increases sensing reliability in poor weather and lighting conditions. State-of-theart deep learning methods require training, but the labeling of radar data needed to generate the required "ground truth" is time-consuming and requires expert knowledge, due to the confusing nature of radar signals. This limits the development of accurate radar models. To address the difficulty of annotating radar datasets, we propose a novel semi-supervised learning framework we call Automotive Radar Consistency (ARC), which enables the inclusion of unlabeled radar frames during training. This improves performance without adding manual labor. Our model learns object features based on micro-Doppler signatures from a time series of radar frames. We posit that the same object in both the forward and reverse temporal directions will share similar features, and we use this as a consistency regularization to encourage the model to learn how to distinguish targets. In addition, we propose Focal JS-divergence to alleviate over-fitting and address the class imbalance problem in semisupervised learning, by focusing on the hard classes and samples in the unlabeled data. Our proposed method is a general training strategy that can be incorporated in most existing deep learning frameworks. Our experimental results on two public datasets with limited annotations show that our method significantly improves the performance of existing supervised methods. Under the same network architecture, our method outperforms the fully supervised method in object detection by as much as 36%.

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Semisupervised Radar-Based Gait Recognition in the Wild via Ensemble Determination Strategy

Cited by 2 publications

References 0 publications

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

ARC: Automotive Radar Consistency Regularization for Semi-Supervised Learning

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