Robust Hand Gesture Recognition with the Depth Camera
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- čas přidán 3. 08. 2013
- This is the recent work for hand gesture recognition from Prof. Junsong Yuan's Computer vision group in Nanyang Technological University, Singapore. The program can recognize ten digit gestures at a frame rate of more 15FPS using a depth camera.
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Great work Prof. Hui Liang, it is very interesting. Quite some time ago I also develop a software for hands gestures/movement detection but I use a normal and very cheap webcam. You can have a look at it in my channel.
I guess i will take the idea of using a depth camera I think it could make it much easier.
I am really liked your project
你好，请问哪里可以下载您的这篇论文啊，搜了很久都没有发现，能发一份吗?
how do you recognise hand at first place? skin detection? depth filtering? CNN?
yoo here your detecting hand via skin color or any other technique ?
heep on the great work
great job if you would like to share the code
hello, how are you, is good project, did you have the code of this project, i have project like this.
hi can you send me the code please. I really need it.
What camera are you using? Can Intel RealSense 3D camera be used for the same purpose?
Mona Jalal Can I use web camera?? Do u have emgu cv hand detection projects??? email me gihansalith3@gmail.com
Mona Jalal yes, the depth image are similar
Gihan Wijethunga no, it is designed for depth camera.
hey perfect project. can't we do this with normal web cam camera ?
i want to ask
i have project to create alphabet sign language recognition. this recognition use to translate the image from webcam to the mean, and show in alphabet.
but i'm not understand how to make computer know what i mean
maybe you can explain more sir
thank you
Indra Pratama Basically, you need to first capture the image from the webcam, then extract certain image features from it, and use some classifiers such as a SVM to classify the image features to the corresponding category. To do this you will have to collect some labeled training data and train the classifier. There are many tutorials and you can google it with "vision-based hand gesture recognition"
hi, i'm confuse when you said depth camera, what is depth camera exactly?
is that the same kind of camera (webcam) in general there on market?
I think he said a softkinetic depth camera.
like this??
www.softkinetic.com/
is that a hardware camera??
Hsieh Hunter
like this??
www.softkinetic.com/
is that a hardware camera??
Yeah, like a ds325.
oke thanks..
For those who are interested, we have another reference implementation, and the details can be found here:
Hand Parsing and Gesture Recognition with the Depth Camera. Test Code Available!
projects | seraphlh
Thanks!
Hui Liang The depth camera is Kinect? or other device? Thx!
+闫振雷 it is Softkinetic ds325
hello you. at present I have process about Real-Time Static Hand Gesture Recognition, can you help me with your source code. please? I very need. Thanks, good luck to you and health !!
Can i get uour source code?thx for your help
sir do u have the algorithm and source cord of this project .
I have found this website and i think you will be find your problem
www.ilmuotomasi.com/2017/01/soft-computing-hand-gesture-recognition.html
here your detect the hand via skin colour or edge base ?
+Gayan Perera it is based on pixel-level classification of depth images, you can refer to the code for pixel-level hand and body segmentation in my web site: sites.google.com/site/seraphlh/downloads
hi, pls can u share the code with me
Can it be used on phones? Please tell me it can
Bernardus Muller this version cannot, as it is based on depth cameras, which is usually not available on phones. You can check our more recent work here: czcams.com/video/-qld-DeVf7c/video.html It uses a color camera as the input with quite low complexity, and should also work on phones.
Hello, can you send me your source code for study? Asamis Sushi
Hui Liang can you send me the code project. was it implemented with .net
I have found this website and i think you will be find your problem
www.ilmuotomasi.com/2017/01/soft-computing-hand-gesture-recognition.html