Open-set human activity recognition based on micro-Doppler signatures

Yang, Yang; Hou, Chunping; Lang, Yue; Guan, Dai; Huang, Danyang; Xu, Jinchen

doi:10.1016/j.patcog.2018.07.030

Cited by 129 publications

(49 citation statements)

References 33 publications

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“…Next, we first give a review from the discriminative model perspective, where most existing OSR algorithms are modeled from this perspective. Deep Neural Network-based [25], [64], [79]- [87] Generative model Instance Generation-based [62], [88]- [91] Non-Instance Generation-based [63] A. Discriminative Model for OSR 1) Traditional ML Methods-based OSR Models: As mentioned above, traditional machine learning methods (e.g., SVM, sparse representation, Nearest Neighbor, etc.) usually assume that the training and testing data are drawn from the same distribution.…”

Section: A Categorization Of Osr Techniquesmentioning

confidence: 99%

“…ASG can be applied to various learning models besides neural networks, while it can generate not only UUCs' data but also KKCs' data if necessary. In addition, Yang et al [62] borrowed the generator in a typical GAN networks to produce synthetic samples that are highly similar to the target samples as the automatic negative set, while the discriminator is redesigned to output multiple classes together with an UUC. Then they explored the open set human activity recognition based on micro-Doppler signatures.…”

Section: B Generative Model For Open Set Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Open Set Recognition: A Survey

Geng

Huang

Chen

2021

IEEE Trans. Pattern Anal. Mach. Intell.

590

268

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In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with the unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also overview the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.

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Section: A Categorization Of Osr Techniquesmentioning

confidence: 99%

Section: B Generative Model For Open Set Recognitionmentioning

confidence: 99%

Recent Advances in Open Set Recognition: A Survey

Geng

Huang

Chen

2021

IEEE Trans. Pattern Anal. Mach. Intell.

590

268

View full text Add to dashboard Cite

show abstract

“…Yet, DNN architectures in RF applications are often limited by the fact that only small datasets are available for training, as data acquisition can be time consuming, costly, and limited in terms of the scope of scenarios and targets sampled. This impacts not only DNN depth, but also the ability of the DNN to generalize across different body types, speeds, and motion classes [20], as well as adapt to different noise sources and environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Motion Classification Using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures

Erol

Gürbüz

Amin

2020

IEEE Trans. Aerosp. Electron. Syst.

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Deep neural networks (DNNs) have recently received vast attention in applications requiring classification of radar returns, including radar-based human activity recognition for security, smart homes, assisted living, and biomedicine. However, acquiring a sufficiently large training dataset remains a daunting task due to the high human costs and resources required for radar data collection. In this paper, an extended approach to adversarial learning is proposed for generation of synthetic radar micro-Doppler signatures that are well-adapted to different environments. The synthetic data is evaluated using visual interpretation, analysis of kinematic consistency, data diversity, dimensions of the latent space, and saliency maps. A principlecomponent analysis (PCA) based kinematic-sifting algorithm is introduced to ensure that synthetic signatures are consistent with physically possible human motions. The synthetic dataset is used to train a 19-layer deep convolutional neural network (DCNN) to classify micro-Doppler signatures acquired from an environment different from that of the dataset supplied to the adversarial network. An overall accuracy 93% is achieved on a dataset that contains multiple aspect angles (0 • , 30 • , and 45 • as well as 60 • ), with 9% improvement as a result of kinematic sifting.B. Erol is currently with Siemens Corporate Technology, Princeton, NJ. Formerly, he was with the Center for Advanced Communications, Villanova University, PA, email: baris.erol@siemens.com.S.Z. Gurbuz is with the

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“…In recent years, there has been a great research interest in human activity classification using micro-Doppler signatures [10], [11], [12], [13]. In [14], a low-power pulse-Doppler radar that operates at 5.8 GHz was used to collect the micro-Doppler signatures of three different activities (walking, running, and crawling) performed by four subjects on a treadmill.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Detection and Coarse Localization Outdoors Using Micro-Doppler Signatures

2019

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Human activity detection outdoors is emerging as a very important research field due to its potential application in surveillance, assisted living, search and rescue, and military applications. For such applications it is important to have detailed information about the human target, for example, whether the detected target is a single person or a group of people, what activity a target is performing, and the rough location of the target. In this paper, we propose novel usage of machine learning techniques to perform subject classification, human activity classification, people counting, and coarse localization by classifying micro-Doppler signatures obtained from a low-cost and low-power radar system. Our experiments were performed outdoors. For feature extraction of micro-Doppler signatures, we applied a two-directional two-dimensional principle component analysis (2D2PCA). Our results show that by applying 2D2PCA, the accuracy results of Support Vector Machine (SVM) and knearest neighbors (kNN) classifiers were greatly improved. We also designed and implemented a Convolutional Neural Network (CNN) for the target classifications in terms of type, number, activity and coarse localization. Our CNN model obtained very high classification accuracies (97% to 100%), which are superior to the best results obtained by SVM and kNN. Finally, we investigated the effects of the frame length of the sliding window, the angle of the direction of movement, and the number of radars used on the classification performance, providing valuable guidelines for machine learning modeling and experimental setup of micro-Doppler based research and applications.

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Open-set human activity recognition based on micro-Doppler signatures

Cited by 129 publications

References 33 publications

Recent Advances in Open Set Recognition: A Survey

Recent Advances in Open Set Recognition: A Survey

Motion Classification Using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures

Human Activity Detection and Coarse Localization Outdoors Using Micro-Doppler Signatures

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