Adaptive hough transform with optimized deep learning followed by dynamic time warping for hand gesture recognition

Kowdiki, Manisha; Khaparde, Arti

doi:10.1007/s11042-021-11469-9

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…Table 5 presents the modified DSC block in DSCNN, which is used in the areas with more network input or output channels. In Equation (11), ∆S is below zero if N < M or K 2 (N −M) < M * N. It implies the increase of parameter obtained after DSC compared with conventional convolution, when the number of input channels is more than the number of output channels or both numbers are large. In this paper, DSC is employed to address the large number of channels.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Hough transform maps the lines crossing a point ( x , y ) in a rectangular coordinate system into parameter space [11]. The polar coordinate equation for the lines crossing ( x , y ) is

\begin{array}{l}\rho =x\ast \cos \theta +y\ast \sin \theta \hfill \end{array}

…”

Section: Formation Recognitionmentioning

confidence: 99%

“…Hough transform maps the lines crossing a point (x, y) in a rectangular coordinate system into parameter space [11]. The polar coordinate equation for the lines crossing (x, y) is…”

Section: Formation Image Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Warship formation extraction and recognition based on density‐based spatial clustering of applications with noise and improved convolutional neural network

2022

IET Radar Sonar & Navi

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Formation recognition is a significant focus of maritime target recognition. Automatic formation extraction and recognition facilitate autonomous decision-making. However, few studies have explored formation extraction prior to recognition. This paper introduces a density-based spatial clustering of applications with noise (DBSCAN) method based on Gaussian kernel to extract formation targets. On this basis, a depthwise separable convolutional neural network (DSCNN) method is proposed for formation recognition. A track simulation system is established to form a track dataset containing three different proportions of clutter, and the formation extraction method is examined using track dataset. Subsequently, the image dataset with eight different types of formation is formulated, on the basis of various detection errors, the DSCNN method for formation recognition is compared with several typical deep learning methods. As exposed in experimental results, the DBSCAN method based on Gaussian kernel can guarantee accurate extraction of formation targets subject to different proportions of clutter. Hence, it is greatly robust and capable of effective formation extraction. Under different radar detection errors, the formation recognition accuracy of DSCNN is 91.5%-99.5%, which achieves performance improvement by up to 12.5% compared with other deep learning methods. The combination of DBSCAN and DSCNN can well realise formation extraction and recognition with different proportions of clutter in tracks and various radar detection errors.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

“…Hough transform maps the lines crossing a point ( x , y ) in a rectangular coordinate system into parameter space [11]. The polar coordinate equation for the lines crossing ( x , y ) is

\begin{array}{l}\rho =x\ast \cos \theta +y\ast \sin \theta \hfill \end{array}

…”

Section: Formation Recognitionmentioning

confidence: 99%

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Warship formation extraction and recognition based on density‐based spatial clustering of applications with noise and improved convolutional neural network

2022

IET Radar Sonar & Navi

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“…The Transformer mode performs better gesture recognition than other neural networks for time series of radar data. The research in [ 29 ] achieves better results by introducing a Transformer for the sequence modeling of hand gestures. Biao Jin utilizes micro-Doppler maps to train the 2DCNN+Transformer model for gesture classification with 98% accuracy [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

TRANS-CNN-Based Gesture Recognition for mmWave Radar

Zhang,

Liu,

Zhang

et al. 2024

Sensors

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In order to improve the real-time performance of gesture recognition by a micro-Doppler map of mmWave radar, the point cloud based gesture recognition for mmWave radar is proposed in this paper. Two steps are carried out for mmWave radar-based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave radar sensor IWR1642 is used as a benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx radar sensor is developed in our lab, and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real time with limited training data in comparison with the existing methods.

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“…Since Hough earned a patent in 1962 for his method that allows efficient detection of lines in images [1] , which we now call Hough transform (HT), more than 2500 papers have been devoted to its variants. HT has since been applied to various fields, such as image analysis [2,3], auto-piloting [4], artificial intelligence [5] and data clustering [6,7].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Fast Hough Transform for Multidimensional Data

Gan

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Line, plane and hyperplane detection in multidimensional data has many applications in computer vision and artificial intelligence. We propose Integrated Fast Hough Transform (IFHT), a highly-efficient multidimensional Hough transform algorithm based on a new mathematical model. The parameter space of IFHT can be represented with a single k-tree to support hierarchical storage and "coarse-to-fine" search strategy. IFHT essentially changes the least square data-fitting in Li's Fast Hough transform (FHT) to the total least squares data-fitting, in which observational errors across all dimensions are taken into account, thus more practical and more resistant to data noise. It has practically resolved the problem of decreased precision of FHT for target objects mapped to boundaries between accumulators in the parameter space. In addition, it enables a straightforward visualization of the parameter space which not only provides intuitive insight on the number of objects in the data, but also helps with tuning the parameters and combining multiple instances if needed. In all simulated data with different levels of noise and parameters, IFHT surpasses Li's Fast Hough transform in terms of robustness and precision significantly.

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Adaptive hough transform with optimized deep learning followed by dynamic time warping for hand gesture recognition

Cited by 12 publications

References 34 publications

Warship formation extraction and recognition based on density‐based spatial clustering of applications with noise and improved convolutional neural network

Warship formation extraction and recognition based on density‐based spatial clustering of applications with noise and improved convolutional neural network

TRANS-CNN-Based Gesture Recognition for mmWave Radar

An Integrated Fast Hough Transform for Multidimensional Data

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