Combinations of eigenspace and GMM with Kinect sensor-extracted action gesture features for person identity recognition

2016 ◽  
Vol 33 (8) ◽  
pp. 2489-2503 ◽  
Author(s):  
Ing-Jr Ding ◽  
Zong-Gui Wu
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Optimal Performance ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Feature Design ◽  
Content Type ◽  
Active User ◽  
Individual Classifier ◽  
User Recognition

Purpose The Kinect sensor released by Microsoft is well-known for its effectiveness on human gesture recognition. Gesture recognition by Kinect has been proved to be an efficient command operation and provides an additional human-computer interface in addition to the traditional speech recognition. For Kinect gesture recognition in the application of gesture command operations, recognition of the active user making the gesture command to Kinect will be an extremely crucial problem. The purpose of this paper is to propose a recognition method for recognizing the person identity of an active user using combined eigenspace and Gaussian mixture model (GMM) with Kinect-extracted action gesture features. Design/methodology/approach Several Kinect-derived gesture features will be explored for determining the effective pattern features in the active user recognition task. In this work, a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of each individual classifier. In addition to Kinect-extracted feature designs for active user recognition, this study will further develop a combined recognition method, called combined eigenspace-GMM, which properly hybridizes the decision information of both the eigenspace and the GMM for making a more reliable user recognition result. Findings Active user recognition using an effective combination of eigenspace and GMM with well-developed active gesture features in Kinect-based active user recognition will have an outstanding performance on the recognition accuracy. The presented Kinect-based user recognition system using the presented approach will further have the competitive benefits of recognition on both gesture commands and providing users of gesture commands. Originality/value A hybridized scheme of eigenspace and GMM performs better than eigenspace-alone or GMM-alone on recognition accuracy of active user recognition; a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of the individual classifier; combined eigenspace-GMM active user recognition belonging to model-based active user recognition design has a fine extension on increasing the recognition rate by adjusting recognition models.

A Track-Based Gesture Recognition Algorithm for Kinect

2015 ◽  
Vol 738-739 ◽  
pp. 334-338 ◽  
Author(s):  
Ying Wang ◽  
Ling Zhang
Keyword(s):  
Gesture Recognition ◽  
Golden Section ◽  
Recognition Algorithm ◽  
Depth Image ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Image Information ◽  
Golden Section Search ◽  
Highly Effective

This paper presents a new gesture track recognition method based on the depth image information received from the Kinect sensor. First, a Kinect sensor is used to obtain the coordinates of a moving arm. Then, the gesture tracks corresponding to these coordinates are analyzed. Matching and recognition of gesture tracks are implemented by performing golden section search. The results show that this track-based method is highly effective in gesture recognition.

Human robot interaction for manipulation tasks based on stroke gesture recognition

2017 ◽  
Vol 44 (6) ◽  
pp. 700-710 ◽  
Author(s):  
Jiajun Li ◽  
Jianguo Tao ◽  
Liang Ding ◽  
Haibo Gao ◽  
Zongquan Deng ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Design Methodology ◽  
Spatial Information ◽  
Recognition Accuracy ◽  
Parameter Extraction ◽  
Human Robot Interaction ◽  
Extraction System ◽  
Robot Interaction ◽  
Content Type ◽  
Practical Implications

Purpose The purpose of this paper is to extend the usage of stroke gestures in manipulation tasks to make the interaction between human and robot more efficient. Design/methodology/approach In this paper, a set of stroke gestures is designed for typical manipulation tasks. A gesture recognition and parameter extraction system is proposed to exploit the information in stroke gestures drawn by the users. Findings The results show that the designed gesture recognition subsystem can reach a recognition accuracy of 99.00 per cent. The parameter extraction subsystem can successfully extract parameters needed for typical manipulation tasks with a success rate about 86.30 per cent. The system shows an acceptable performance in the experiments. Practical implications Using stroke gesture in manipulation tasks can make the transmission of human intentions to the robots more efficient. The proposed gesture recognition subsystem is based on convolutional neural network which is robust to different input. The parameter extraction subsystem can extract the spatial information encoded in stroke gestures. Originality/value The author designs stroke gestures for manipulation tasks which is an extension of the usage of stroke gestures. The proposed gesture recognition and parameter extraction system can make use of stroke gestures to get the type of the task and important parameters for the task simultaneously.

Discriminative bit selection hashing in RGB-D based object recognition for robot vision

2019 ◽  
Vol 39 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Lin Feng ◽  
Yang Liu ◽  
Zan Li ◽  
Meng Zhang ◽  
Feilong Wang ◽  
...  
Keyword(s):  
Object Recognition ◽  
Recognition Accuracy ◽  
Robot Vision ◽  
Binary Representation ◽  
Recognition Method ◽  
Content Type ◽  
Selection Pattern ◽  
Approximate Nearest Neighbors ◽  
Label Information ◽  
High Recognition Accuracy

PurposeThe purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.Design/methodology/approachTo promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.FindingsThe experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.Originality/valueHashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.

scholarly journals Early gesture recognition method with an accelerometer

2015 ◽  
Vol 11 (3) ◽  
pp. 270-287 ◽  
Author(s):  
Ryo Izuta ◽  
Kazuya Murao ◽  
Tsutomu Terada ◽  
Masahiko Tsukamoto
Keyword(s):  
Gesture Recognition ◽  
Early Stage ◽  
The Other ◽  
Training Data ◽  
Recognition Method ◽  
Content Type ◽  
Recognition Process ◽  
Recognition Result ◽  
Interface Usability ◽  
Proper Length

Purpose – This paper aims to propose a gesture recognition method at an early stage. An accelerometer is installed in most current mobile phones, such as iPhones, Android-powered devices and video game controllers for the Wii or PS3, which enables easy and intuitive operations. Therefore, many gesture-based user interfaces that use accelerometers are expected to appear in the future. Gesture recognition systems with an accelerometer generally have to construct models with user’s gesture data before use and recognize unknown gestures by comparing them with the models. Because the recognition process generally starts after the gesture has finished, the output of the recognition result and feedback delay, which may cause users to retry gestures, degrades the interface usability. Design/methodology/approach – The simplest way to achieve early recognition is to start it at a fixed time after a gesture starts. However, the degree of accuracy would decrease if a gesture in an early stage was similar to the others. Moreover, the timing of a recognition has to be capped by the length of the shortest gesture, which may be too early for longer gestures. On the other hand, retreated recognition timing will exceed the length of the shorter gestures. In addition, a proper length of training data has to be found, as the full length of training data does not fit the input data until halfway. To recognize gestures in an early stage, proper recognition timing and a proper length of training data have to be decided. This paper proposes a gesture recognition method used in the early stages that sequentially calculates the distance between the input and training data. The proposed method outputs the recognition result when one candidate has a stronger likelihood of recognition than the other candidates so that similar incorrect gestures are not output. Findings – The proposed method was experimentally evaluated on 27 kinds of gestures and it was confirmed that the recognition process finished 1,000 msec before the end of the gestures on average without deteriorating the level of accuracy. Gestures were recognized in an early stage of motion, which would lead to an improvement in the interface usability and a reduction in the number of incorrect operations such as retried gestures. Moreover, a gesture-based photo viewer was implemented as a useful application of our proposed method, the proposed early gesture recognition system was used in a live unscripted performance and its effectiveness is ensured. Originality/value – Gesture recognition methods with accelerometers generally learn a given user’s gesture data before using the system, then recognizes any unknown gestures by comparing them with the training data. The recognition process starts after a gesture has finished, and therefore, any interaction or feedback depending on the recognition result is delayed. For example, an image on a smartphone screen rotates a few seconds after the device has been tilted, which may cause the user to retry tilting the smartphone even if the first one was correctly recognized. Although many studies on gesture recognition using accelerometers have been done, to the best of the authors’ knowledge, none of these studies has taken the potential delays in output into consideration.

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

2020 ◽  
Vol 29 (6) ◽  
pp. 1153-1164
Author(s):  
Qianyi Xu ◽  
Guihe Qin ◽  
Minghui Sun ◽  
Jie Yan ◽  
Huiming Jiang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Feature Fusion ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Method

scholarly journals A Novel Gesture Recognition System Based on CSI Extracted from a Smartphone with Nexmon Firmware

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 222
Author(s):  
Tao Li ◽  
Chenqi Shi ◽  
Peihao Li ◽  
Pengpeng Chen
Keyword(s):  
Frequency Domain ◽  
Gesture Recognition ◽  
Channel State Information ◽  
Cross Correlation ◽  
Recognition Accuracy ◽  
Correlation Method ◽  
Bottom Layer ◽  
Recognition System ◽  
Channel State ◽  
State Information

In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system’s mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system’s recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy.

scholarly journals A Robot Object Recognition Method Based on Scene Text Reading in Home Environments

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1919
Author(s):  
Shuhua Liu ◽  
Huixin Xu ◽  
Qi Li ◽  
Fei Zhang ◽  
Kun Hou
Keyword(s):  
Object Recognition ◽  
Recognition Accuracy ◽  
Multiple Objects ◽  
Recognition Method ◽  
Home Environments ◽  
Complex Scenes ◽  
Scene Text ◽  
Text Reading ◽  
Text Images ◽  
Chinese And English

With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the model is trained by these two datasets. Finally, a robot object recognition method is proposed based on the scene text reading. The robot detects and recognizes texts in the image and then stores the recognition results in a text file. When the user gives the robot a fetching instruction, the robot searches for corresponding keywords from the text files and achieves the confidence of multiple objects in the scene image. Then, the object with the maximum confidence is selected as the target. The results show that the robot can accurately distinguish objects with arbitrary shape and category, and it can effectively solve the problem of object recognition in home environments.

Towards ubiquitous human gestures recognition using wireless networks

2017 ◽  
Vol 13 (4) ◽  
pp. 408-418 ◽  
Author(s):  
Mustafa S. Aljumaily ◽  
Ghaida A. Al-Suhail
Keyword(s):  
Gesture Recognition ◽  
Channel State Information ◽  
Device Driver ◽  
Recognition System ◽  
Wireless Channel ◽  
New Classification ◽  
Channel State ◽  
State Information ◽  
Content Type ◽  
Human Gestures

Purpose Recently, many researches have been devoted to studying the possibility of using wireless signals of the Wi-Fi networks in human-gesture recognition. They focus on classifying gestures despite who is performing them, and only a few of the previous work make use of the wireless channel state information in identifying humans. This paper aims to recognize different humans and their multiple gestures in an indoor environment. Design/methodology/approach The authors designed a gesture recognition system that consists of channel state information data collection, preprocessing, features extraction and classification to guess the human and the gesture in the vicinity of a Wi-Fi-enabled device with modified Wi-Fi-device driver to collect the channel state information, and process it in real time. Findings The proposed system proved to work well for different humans and different gestures with an accuracy that ranges from 87 per cent for multiple humans and multiple gestures to 98 per cent for individual humans’ gesture recognition. Originality/value This paper used new preprocessing and filtering techniques, proposed new features to be extracted from the data and new classification method that have not been used in this field before.

One Extended OTSU Flame Image Recognition Method Using RGBL and Stripe Segmentation

2011 ◽  
Vol 121-126 ◽  
pp. 2141-2145 ◽  
Author(s):  
Wei Gang Yan ◽  
Chang Jian Wang ◽  
Jin Guo
Keyword(s):  
Image Segmentation ◽  
Water Vapor ◽  
Image Recognition ◽  
Recognition Accuracy ◽  
New Method ◽  
Recognition Method ◽  
Otsu’S Method ◽  
Flame Shape ◽  
Image Segmentation Algorithm ◽  
Rgb Image

This paper proposes a new image segmentation algorithm to detect the flame image from video in enclosed compartment. In order to avoid the contamination of soot and water vapor, this method first employs the cubic root of four color channels to transform a RGB image to a pseudo-gray one. Then the latter is divided into many small stripes (child images) and OTSU is employed to perform child image segmentation. Lastly, these processed child images are reconstructed into a whole image. A computer program using OpenCV library is developed and the new method is compared with other commonly used methods such as edge detection and normal Otsu’s method. It is found that the new method has better performance in flame image recognition accuracy and can be used to obtain flame shape from experiment video with much noise.

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