Hand CV Gesture Control for VR Developed by Beijing Yingmeiji Technology CO., Ltd.
Hand CV realizes gesture control based on regular monocular camera and pure software algorithm. Unlike the normal solutions, our gesture recognition algorithm is not only based on skin-color, but also based on the outline of our hands and machine learning. By adding RGB detecting algorithm, HOG algorithm and YOLO detecting algorithm, we come up with a much better solution for hand detection. The result shows that our algorithm can reach a better performance than the others.
The form of the company’s product is an SDK. If you use our SDK in the VR product(whether software or hardware ), your users can use gesture control without using any additional device. Some examples of using gesture control in VR environemnt could be using gestures to play/pause video, control the progress bar by dragging, select video or app. All of these can be achieved by gesture control within the VR environment if you integrate our SDK in VR product.
Check Hand CV Official Website: http://www.handcv.com/en.html
The team has more than ten years’ experience in conducting research and development in the field of Computer Vision. Hand CV is the new product that aims to solve the interaction pain points in mobile VR environment. The team believes adding gesture control in every mobile VR product can greatly improve and enrich the user experience of it.
- Basic Info
Product name: Hand CV Gesture Interaction SDK
Developer: Beijing Yingmeiji Technology CO., Ltd.
Software platforms: IOS, Android, Unity, Windows, Linux, Mac OS
Hand CV is specially optimized for mobile VR / AR. It has strong robustness and is easy to use, which satisfies the needs of light interaction, such as selecting, confirm, dragging and dropping. With Hand CV, users can experience the convenience of gesture interaction without paying a penny.
Unlike common solutions, Hand CV’s gesture recognition algorithm is not merely based on skin-color detection, but also based on the outline of our hands and machine learning. By adding RGB detecting algorithm, HOG algorithm and YOLO detecting algorithm, Hand CV comes up with a much better solution for hand detection. The result shows that Hand CV reaches a higher recognition rate and lower error rate than most commercial hand recognition solutions.
The team of Hand CV believes that gesture interaction is one of the best interaction ways in mobile VR/AR. In reality, we use hands to interact with the objects around us. Bringing people’s hands into mobile VR/AR environment can make us feel more immersed.
Hand CV is looking for partners to build awesome products using Hand CV’s Gesture Interaction SDK. The SDK will be open for commercial use in September, 2016.
HandCV Gesture Control Detail
- Product Intro
Hand CV is a Gesture Recognition System and SDK based on monocular camera.
It’s based on ordinary monocular camera (such as ordinary camera of phone, tablet, PC and other devices), using pure software algorithms to achieve gesture control. Unlike the other gesture control solutions, Hand CV do not require any additional device.
Hand CV is specially optimized for mobile VR / AR environment. It has strong robustness and is easy to use, which can satisfy the needs of light interaction, such as selecting, confirm, dragging and dropping. With Hand CV, users can enjoy the convenience of gesture interaction without paying a penny.
- Product Characteristics
Hand CV is mainly designed to meet the needs of light interaction, such as to confirm, click and drag. Hand CV has high robustness and is easy to use, making the interaction in mobile VR/AR more natural, efficient and convenient. In addition to mobile VR/AR, Hand CV will open their gesture recognition SDK to all fields, and provides support for multi-platforms.
Hand CV realizes gesture control based on regular monocular camera. Unlike the normal solutions, our gesture recognition algorithm is not only based on skin-color detection, but also based on the outline of our hands and machine learning. By adding RGB detecting algorithm, HOG algorithm and YOLO detecting algorithm, we come up with a much better solution for hand detection. The result shows that our algorithm can reach a better performance than the others.
- Background
With the launching of Oculus, Cardboard and Google glass to market in 2014, mobile VR / AR devices become amazingly popular. However, the interaction in mobile VR/AR still have many problems. Gaze control (Cardboard), trackpad (Samsung Gear VR) and controller (such as Daydream) all have some inherited limitations.
We believe that the gesture interaction is the most appropriate way to interact in mobile VR / AR environment. Using hands to interact with virtual objects can make people feel more immersed. From a technical point of view, even though many gesture control techology can been found in the market, almost all of them requires the users to purchase depth camera, which cost around $100. Therefore, we developed Hand CV, a gesture control system that only needs the single built-in camera on the device.
