scholarly journals Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xuhong Ma ◽  
Jinzhu Peng
Keyword(s):  
Convex Hull ◽  
Real Time ◽  
Gesture Recognition ◽  
Detection Algorithm ◽  
Depth Information ◽  
Hand Position ◽  
Improved Method ◽  
Long Distance ◽  
Scanning Method ◽  
Fingertip Detection

Gesture recognition is an important part of human-robot interaction. In order to achieve fast and stable gesture recognition in real time without distance restrictions, this paper presents an improved threshold segmentation method. The improved method combines the depth information and color information of a target scene with hand position by the spatial hierarchical scanning method; the ROI in the scene is thus extracted by the local neighbor method. In this way, the hand can be identified quickly and accurately in complex scenes and different distances. Furthermore, the convex hull detection algorithm is used to identify the positioning of fingertips in ROI, so that the fingertips can be identified and located accurately. The experimental results show that the hand position can be obtained quickly and accurately in the complex background by using the improved method, the real-time recognition distance interval can be reached by 0.5 m to 2.0 m, and the fingertip detection rates can be reached 98.5% in average. Moreover, the gesture recognition rates are more than 96% by the convex hull detection algorithm. It can be thus concluded that the proposed method achieves good performance of hand detection and positioning at different distances.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Real-Time 3D Hand Gesture Recognition from Depth Image

2013 ◽  
Vol 765-767 ◽  
pp. 2826-2829 ◽  
Author(s):  
Song Lin ◽  
Rui Min Hu ◽  
Yu Lian Xiao ◽  
Li Yu Gong
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Point Cloud ◽  
Hand Gesture Recognition ◽  
Microsoft Kinect ◽  
Depth Image ◽  
Support Vector ◽  
Hand Gesture ◽  
Depth Information ◽  
Moment Invariant

In this paper, we propose a novel real-time 3D hand gesture recognition algorithm based on depth information. We segment out the hand region from depth image and convert it to a point cloud. Then, 3D moment invariant features are computed at the point cloud. Finally, support vector machine (SVM) is employed to classify the shape of hand into different categories. We collect a benchmark dataset using Microsoft Kinect for Xbox and test the propose algorithm on it. Experimental results prove the robustness of our proposed algorithm.

scholarly journals Real Time Vision Based Fingertip Detection Strategies

2019 ◽  
Vol 9 (2) ◽  
pp. 3532-3536
Keyword(s):  
Virtual Reality ◽  
Augmented Reality ◽  
Real Time ◽  
Gesture Recognition ◽  
Smart Home ◽  
Robot Control ◽  
Research Directions ◽  
Detection And Tracking ◽  
Fingertip Detection ◽  
Detection Strategies

The Fingertip Detection acts a specific role in most of the vision based applications. The latest technologies like virtual reality and augmented reality actually follows this fingertip detection concept as its foundation. It is also helpful for Human Computer Interaction (HCI). So fingertip detection and tracking can be applied from games to robot control, from augmented reality to smart homes. The most important interesting field of fingertip detection is the gesture recognition related applications. In the context of interaction with the machines, gestures are the most simplest and efficient means of communication. This paper analyses the various works done in the areas of fingertip detection. A review on various real time fingertip methods is explained with different techniques and tools. Some challenges and research directions are also highlighted. Many researchers uses fingertip detection in HCI systems those have many applications in user identification, smart home etc. A comparison of results by different researchers is also included.

scholarly journals A Novel Hand Gesture Recognition Based on High-Level Features

2018 ◽  
Vol 15 (02) ◽  
pp. 1750022 ◽  
Author(s):  
Jing Li ◽  
Jianxin Wang ◽  
Zhaojie Ju
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Gesture Recognition ◽  
Feature Fusion ◽  
Recognition System ◽  
Depth Information ◽  
Static Gesture Recognition ◽  
Time Requirements ◽  
High Level ◽  
Computer Interaction

Gesture recognition plays an important role in human–computer interaction. However, most existing methods are complex and time-consuming, which limit the use of gesture recognition in real-time environments. In this paper, we propose a static gesture recognition system that combines depth information and skeleton data to classify gestures. Through feature fusion, hand digit gestures of 0–9 can be recognized accurately and efficiently. According to the experimental results, the proposed gesture recognition system is effective and robust, which is invariant to complex background, illumination changes, reversal, structural distortion, rotation, etc. We have tested the system both online and offline which proved that our system is satisfactory to real-time requirements, and therefore it can be applied to gesture recognition in real-world human–computer interaction systems.

scholarly journals Moving-Vehicle Identification Based on Hierarchical Detection Algorithm

2021 ◽  
Vol 14 (1) ◽  
pp. 264
Author(s):  
Zhifa Yang ◽  
Yu Zhu ◽  
Haodong Zhang ◽  
Zhuo Yu ◽  
Shiwu Li ◽  
...  
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Sliding Window ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Scanning Method ◽  
Moving Vehicle ◽  
The Real ◽  
Time Performance ◽  
Moving Vehicles

The vehicle detection method plays an important role in the driver assistance system. Therefore, it is very important to improve the real-time performance of the detection algorithm. Nowadays, the most popular method is the scanning method based on sliding window search, which detects the vehicle from the image to be detected. However, the existing sliding window detection algorithm has many drawbacks, such as large calculation amount and poor real-time performance, and it is impossible to detect the target vehicle in real time during the motion process. Therefore, this paper proposes an improved hierarchical sliding window detection algorithm to detect moving vehicles in real time. By extracting the region of interest, the region of interest is layered, the maximum and minimum values of the detection window in each layer are set, the flashing frame generated by the layering is eliminated by the delay processing method, and a method suitable for the motion is obtained: the real-time detection algorithm of the vehicle, that is, the hierarchical sliding window detection algorithm. The experiments show that the more layers are divided, the more time is needed, and when the number of detection layers is greater than 7, the time change rate increases significantly. As the number of layers decreases, the detection accuracy rate also decreases, resulting in the phenomenon of a false positive. Therefore, it is determined to meet the requirements of real time and accuracy when the image is divided into 7 layers. It can be seen from the experiment that when the images to be detected are divided into 7 layers and the maximum and minimum values of detection windows are 30 × 30 and 250 × 250, respectively, the number of sub-windows generated is one thirty-seventh of the original sliding window detection algorithm, and the execution time is only one-third of the original sliding window detection algorithm. This shows that the hierarchical sliding window detection algorithm has better real-time performance than the original sliding window detection algorithm.

45. Education at distance: Broadcasting ECG rounds to Southeastern Ontario (BESO Project). An innovative approach for teaching electrocardiography

2007 ◽  
Vol 30 (4) ◽  
pp. 51 ◽  
Author(s):  
A. Baranchuk ◽  
G. Dagnone ◽  
P. Fowler ◽  
M. N. Harrison ◽  
L. Lisnevskaia ◽  
...  
Keyword(s):  
Health Care ◽  
Real Time ◽  
Digital Signal ◽  
Teaching Methodology ◽  
Care Providers ◽  
Long Distance ◽  
Medicine Clerkship ◽  
Time Problem ◽  
The Impact ◽  
Ecg Interpretation

Electrocardiography (ECG) interpretation is an essential skill for physicians as well as for many other health care professionals. Continuing education is necessary to maintain these skills. The process of teaching and learning ECG interpretation is complex and involves both deductive mechanisms and recognition of patterns for different clinical situations (“pattern recognition”). The successful methodologies of interactive sessions and real time problem based learning have never been evaluated with a long distance education model. To evaluate the efficacy of broadcasting ECG rounds to different hospitals in the Southeastern Ontario region; to perform qualitative research to determine the impact of this methodology in developing and maintaining skills in ECG interpretation. ECG rounds are held weekly at Kingston General Hospital and will be transmitted live to Napanee, Belleville, Oshawa, Peterborough and Brockville. The teaching methodology is based on real ECG cases. The audience is invited to analyze the ECG case and the coordinator will introduce comments to guide the case through the proper algorithm. Final interpretation will be achieved emphasizing the deductive process and the relevance of each case. An evaluation will be filled out by each participant at the end of each session. Videoconferencing works through a vast array of internet LANs, WANs, ISDN phone lines, routers, switches, firewalls and Codecs (Coder/Decoder) and bridges. A videoconference Codec takes the analog audio and video signal codes and compresses it into a digital signal and transmits that digital signal to another Codec where the signal is decompressed and retranslated back into analog video and audio. This compression and decompression allows large amounts of data to be transferred across a network at close to real time (384 kbps with 30 frames of video per second). Videoconferencing communication works on voice activation so whichever site is speaking has the floor and is seen by all the participating sites. A continuous presence mode allows each site to have the same visual and audio involvement as the host site. A bridged multipoint can connect between 8 and 12 sites simultaneously. This innovative methodology for teaching ECG will facilitate access to developing and maintaining skills in ECG interpretation for a large number of health care providers. Bertsch TF, Callas PW, Rubin A. Effectiveness of lectures attended via interactive video conferencing versus in-person in preparing third-year internal medicine clerkship students for clinical practice examinations. Teach Learn Med 2007; 19(1):4-8. Yellowlees PM, Hogarth M, Hilty DM. The importance of distributed broadband networks to academic biomedical research and education programs. Acad Psychaitry 2006;30:451-455

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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