Gesture recognition based on sparse representation

Miao, Wei; Li, Gongfa; Sun, Ying; Jiang, Guozhang; Kong, Jianyi; Liu, Honghai

doi:10.1504/ijwmc.2016.082289

Cited by 19 publications

(11 citation statements)

References 21 publications

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“…Fang et al 7 further added a motion model based on the improved particle filter framework and combined the target multi-view expression model to track the target. In recent years, people have developed a target tracking algorithm based on sparse expression, 8,9 which is roughly through the sparse expression of the target image, and then through Ll-tracker to achieve effective target tracking. 10 However, the fly in the ointment is that it involves a lot of complexity.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Design and implementation of badminton robot perception and control system

Liu

Wang

2020

International Journal of Advanced Robotic Systems

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With the rapid development of computer technology, target tracking has become an indispensable technology in the field of image processing. Outline-based matching algorithms are one of the most representative methods in the field of computer vision. The idea is to extract several characteristic vectors from the image and compares them with the characteristic vectors in the corresponding image template. The difference between the image and the template characteristic vector is calculated, and the category is determined by the minimum distance method. The badminton robot collects the depth image of the scene through the depth camera and then uses the machine vision theory to process the acquired depth image. To combine the image depth information to obtain the position of the badminton camera coordinate system in the three-dimensional space, the position of the site coordinate system is achieved. Finally, the position information of the badminton in the multi-frame images is used to predict the falling point of the badminton. The badminton positioning and the analysis of the falling point are completed. The badminton robot quickly runs to the predicted position of the badminton and completes a hitting task. To realize the high-speed continuous and smooth badminton action of the badminton robot manipulator, a new multi-objective manipulator trajectory optimization model is proposed. The experimental results show that the new trajectory optimization model can effectively reduce the energy consumption of the motor and improve the rotational efficiency, thus ensuring the response speed of the arm.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Design and implementation of badminton robot perception and control system

Liu

Wang

2020

International Journal of Advanced Robotic Systems

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“…After the original image is segmented by the depth segmentation and the elliptical skin model, a binary image of the gesture image with a large number of backgrounds is obtained, but there are burrs on the gesture boundary or holes in the gesture area, which will interfere with subsequent feature extraction and classification operations [30][31][32][33]. Therefore, it is necessary to perform morphological processing and image enhancement.…”

Section: Post-processing Of Gesture Imagesmentioning

confidence: 99%

Research on gesture recognition of smart data fusion features in the IoT

Tan

Sun

et al. 2019

Neural Comput & Applic

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With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.

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“…With the development of deep learning DNN becomes possible. DNN has many advantages over shallow neural networks, the most obvious of which is its learning ability [41][42]. Therefore, in order to reduce the recognition error rate of the gesture, our recognition task turns to the DNN recognition research of the gesture.…”

Section: Deep Neural Network Recognitionmentioning

confidence: 99%

Jointly network: a network based on CNN and RBM for gesture recognition

Cheng

Sun

et al. 2018

Neural Comput & Applic

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Hand belongs to non-rigid objects and is rich in variety, making gesture recognition more difficult. The essence of dynamic gesture recognition is the classification and recognition of single-frame still images. Therefore, this paper mainly focuses on static gesture recognition. At present, there are some problems in gesture recognition, such as accuracy, real-time or poor robustness. To solve the above problems, in this paper, the Kinect sensor is used to obtain the color and depth gesture samples, and the gesture samples are processed. On this basis, a jointly network of CNN and RBM is proposed for gesture recognition. It mainly uses superposed network of multiple RBMs to carry out unsupervised feature extraction and combined with supervised feature extraction of CNN. Finally, these two features are combined to classify them. The simulation results show that the proposed jointly network has a better performance in identifying simple background gesture samples, and the recognition capability of gesture samples in complex background needs to be improved.

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Gesture recognition based on sparse representation

Cited by 19 publications

References 21 publications

RETRACTED: Design and implementation of badminton robot perception and control system

RETRACTED: Design and implementation of badminton robot perception and control system

Research on gesture recognition of smart data fusion features in the IoT

Jointly network: a network based on CNN and RBM for gesture recognition

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