Human motion classification using a particle filter approach: multiple model particle filtering applied to the micro-Doppler spectrum

Groot, S. R.; Harmanny, R. I. A.; Driessen, Hans; Yarovoy, Alexander

doi:10.1017/s1759078713000366

Cited by 13 publications

(12 citation statements)

References 12 publications

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“…In addition, another fruitful future research direction is a comparison of the performance of a DCNN and all combinations of existing feature-based schemes [6,7,8,9,10,11,12] to investigate the optimum performing methods for micro-Doppler signature-based human activity classification. …”

Section: Discussionmentioning

confidence: 99%

“…In particular, the unique micro-Doppler signatures from human activities enabled diverse and extensive research on human detection and activity classification/analysis using radar sensors [3,4,5,6,7,8,9,10,11,12]. More specifically, the authors of [6] extracted direct micro-Doppler features such as bandwidth and Doppler period, the authors of [7] applied linear predictive code coefficients, and the authors of [8] applied minimum divergence approaches for robust classification under a low signal-to-noise ratio environment.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, the authors of [6] extracted direct micro-Doppler features such as bandwidth and Doppler period, the authors of [7] applied linear predictive code coefficients, and the authors of [8] applied minimum divergence approaches for robust classification under a low signal-to-noise ratio environment. Furthermore, the authors of [9] suggested to use particle filters to extract features, the authors of [10] employed biceptrum-based features, the authors of [11] utilized orthogonal pseudo-Zernike polynomials, and features based on the centroid or the singular value decomposition (SVD) have been exploited in [12]. Compared to optical sensors, the electromagnetic radar sensors can operate in all weather conditions, regardless of lighting changes, and hence are competitive for the applications that require robust operation.…”

Section: Introductionmentioning

confidence: 99%

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Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

Park

Javier

Moon

et al. 2016

Sensors

108

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Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

Park

Javier

Moon

et al. 2016

Sensors

108

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“…Orovic et al applied the Hermite S-method to convert the radar signal into a T-F representation and developed an envelope detection method to capture the evolution of the arm swing [16]. Mobasseri [21]. Given the importance of converting the μ-D signal into a joint T-F representation for classification, the next section describes three T-F analysis methods.…”

Section: B Existing Micro-doppler Classification Approaches For Humamentioning

confidence: 99%

Classification of micro‐Doppler signatures of human motions using log‐Gabor filters

Tivive

Phung

Bouzerdoum

2015

IET Radar, Sonar & Navigation

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In recent years, Doppler radar has been used as a sensing modality for human gait recognition, due to its ability to operate in adverse weather and penetrate opaque obstacles. Doppler radar captures not only the speed of the target, but also the micro-motions of its moving parts. These micro-motions induce frequency modulations that can be used to characterise the target movements. However, a major challenge in Doppler signal processing is to extract discriminative features from the radar returns for target classification. This study presents a feature extraction method for classification of human motions from the micro-Doppler radar signal. The proposed method applies the log-Gabor filters at multiple spatial frequencies and orientations on a joint time-frequency representation. To achieve invariance to the target speed, features are extracted from local patches along the torso Doppler shift. Then, the (2D)2PCA (two-directional two-dimensional principal component analysis) method is applied to create a compact feature vector. Experimental results based on real radar data obtained from multiple human subjects demonstrate the effectiveness of the proposed approach in classifying arm motions.Abstract-In recent years, Doppler radar has been used as a sensing modality for human gait recognition, due to its ability to operate in adverse weather and penetrate opaque obstacles. Doppler radar captures not only the speed of the target, but also the micro-motions of its moving parts. These micro-motions induce frequency modulations that can be used to characterize the target movements. However, a major challenge in Doppler signal processing is to extract discriminative features from the radar returns for target classification. This study presents a feature extraction method for classification of human motions from the micro-Doppler radar signal. The proposed method applies the log-Gabor filters at multiple spatial frequencies and orientations on a joint time-frequency representation. To achieve invariance to the target speed, features are extracted from local patches along the torso Doppler shift. Then, the (2D) 2 PCA method is applied to create a compact feature vector. Experimental results based on real radar data obtained from multiple human subjects demonstrate the effectiveness of the proposed approach in classifying arm motions.Index Terms-Human micro-Doppler signature classification, feature extraction, S-method, Log-Gabor filters. ∞ −∞

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“…Classification of human movements using micro-Doppler signals has been demonstrated for single sensor, monostatic radar systems [1][2][3][4][5][6][7] . Monostatic Doppler radars are capable of responding only to velocity components that are radially oriented towards or away from the radar antenna.…”

Section: Introductionmentioning

confidence: 99%

Determining human target facing orientation using bistatic radar micro-Doppler signals

Fairchild

Narayanan

2014

SPIE Proceedings

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Micro-Doppler radar signals can be used to separate moving human targets from stationary clutter and also to identify and classify human movements. Traditional micro-Doppler radar systems which use a single sensor, monostatic system, suffer from the drawback that only the radial component of the micro-Doppler signal will be observed by the radar operator. This reduces the sensitivity of human activity recognition if the movements are not directly towards or away with respect to the line-of-sight to the radar antenna. In this paper, we propose the use of two bistatic micro-Doppler sensors to overcome this limitation. By using multiple sensors, the orientation of oscillating targets with respect to the radar line-of-sight can be inferred, thereby providing additional information to the radar operator. This approach can be used to infer the facing direction of the human with respect to the radar beam.

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Human motion classification using a particle filter approach: multiple model particle filtering applied to the micro-Doppler spectrum

Cited by 13 publications

References 12 publications

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

Classification of micro‐Doppler signatures of human motions using log‐Gabor filters

Determining human target facing orientation using bistatic radar micro-Doppler signals

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