Real Time Interactive Presentation Apparatus based on Depth Image Recognition

Basori, Ahmad Hoirul; Barukab, Omar

doi:10.11591/ijece.v7i3.pp1308-1315

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The specific algorithm is to conduct a preliminary search first, and then fine search, the search scope is reduced step by step. By setting a fixed threshold, when a mismatch is found, the calculation at the position can be ended in time, thereby reducing the waste of calculation, reducing the calculation, and improving the matching speed [26].…”

Section: ∫∫mentioning

confidence: 99%

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

et al. 2020

View full text Add to dashboard Cite

Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This paper mainly studies the forest image recognition system based on CCD and theodolite. In this paper, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this paper, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target.

show abstract

Section: ∫∫mentioning

confidence: 99%

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Experimental results show that this method has better performance than the latest depth perception salient object segmentation method. In reference [29], the author believes that Kinect has the function of tracking gestures and explaining its actions according to the depth data stream, tracking human gestures in the form of clouds and simultaneously synchronizing. In reference [30], the author proposes a novel imagebased method that reconstructs a set of continuous 3D lines used to create such objects, where each line consists of an ordered set of 3D curve segments.…”

Section: Introductionmentioning

confidence: 99%

Swimmer’s Posture Recognition and Correction Method Based on Embedded Depth Image Skeleton Tracking

Wang

Junhua

Luo

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the continuous emergence of depth image recognition technology, human motion recognition technology has gradually come to life. However, because the current technology of image recognition and skeletal tracking is too backward, people cannot use gesture recognition robots to perform gesture recognition and correction with high accuracy for athletes. In this study, the skeletal tracking depth image is obtained through the Kinect sensor. When using the Kinect sensor to acquire images, the requirements for the environment around the measured object are very low, and it will not be affected by conditions such as light, shadow, and object occlusion, and the pose can be segmented in real time in a complex background. In this study, the depth map feature and the bone information feature are selected for fusion. The HOD feature is difficult to deal with the occlusion problem, and it is not easy to detect the excessive range of the human body gesture or the change of the object direction. Based on this, the HOD feature is improved in many places to form a new 3D-HOD feature and DMM-HOG feature. In this study, the technical research on posture recognition and correction of swimmers in the experiment has realized posture collection, posture segmentation, posture analysis, posture modeling, and posture recognition. In this study, random forests are combined with HOD features to classify pixel-by-pixel points in depth images. The accuracy of training pixel classification is as high as 85%, and the average recognition time of the decision tree for each pose is about 9% higher than that of random forests. The use of Meanshift algorithm to cluster the classified pixels to form skeletal joint points is fast and efficient, and can quickly and accurately find joint points. It has also achieved good experimental effects on human motion recognition and continuous motion recognition, is more suitable for real life, and has commercial practical value.

show abstract

Kinect-Based Real-Time Acquisition Algorithm of Crop Growth Depth Images

Shan

Mei

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Kinect 3D sensing real-time acquisition algorithm that can meet the requirements of fast, accurate, and real-time acquisition of image information of crop growth laws has become the trend and necessary means of digital agricultural production management. Based on this, this paper uses Kinect real-time image generation technology to try to monitor and study the depth map of crop growth law in real time, use Kinect to obtain the algorithm of crop growth depth map, and conduct investigation and research. Real-time image acquisition research on crop growth trends provides a basis for in-depth understanding of the application of Kinect real-time image generation technology in research. Kinect image real-time acquisition algorithm is a very important information carrier in agricultural information engineering. The research results show that the real-time Kinect depth image acquisition algorithm can obtain good 3D image data information and can provide valuable data basis for the 3D reconstruction of the later crop growth model, growth status analysis, and real-time monitoring of crop diseases. The data shows that, using Kinect, the real-time feedback speed of crop growth observation can be increased by 45%, the imaging accuracy is improved by 37%, and the related operation steps are simplified by 30%. The survey results show that the crop yield can be increased by about 12%.

show abstract

Real Time Interactive Presentation Apparatus based on Depth Image Recognition

Cited by 3 publications

References 11 publications

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Swimmer’s Posture Recognition and Correction Method Based on Embedded Depth Image Skeleton Tracking

Kinect-Based Real-Time Acquisition Algorithm of Crop Growth Depth Images

Contact Info

Product

Resources

About