Exploring AI Applications in Online Conference

With the continuous development of Artificial Intelligence (AI) technology, its applications in the field of video are becoming increasingly widespread, bringing numerous innovations to video conferences and other video scenarios. Here are some directions in which AI is advancing in video applications:

 

Video Enhancement

Through AI technology, video quality can be significantly improved. Features such as noise reduction, automatic white balance, and automatic backlight compensation make videos clearer and more realistic. This technology is not only applicable to conferences but also improves online teaching and video presentations.

 

Automatic Tracking and Framing

AI technology can be used to automatically track the movements and positions of participants in a video, ensuring they remain visible in the frame. This functionality is valuable for presentations, training, or any scenario where continuous focus on specific subjects is crucial.

 

Eye Contact

Using AI technology to adjust the direction of participants' eyes towards the camera creates a more face-to-face communication appearance on the screen. This is essential for enhancing the quality of communication and the sense of realism in conferences.

 

Gesture Recognition

AI can recognize specific gestures captured in camera images and trigger corresponding operations. In certain situations, such as training or online teaching, gestures can be used to switch between different AI modes of the camera, improving user convenience.

 

Facial Recognition

In video conferences, facial recognition technology can be applied to scenarios like check-ins, training attendance, and roll calls. This not only enhances the security of conferences but also simplifies some routine processes.

 

Video Content Analysis

AI technology further analyzes video content, such as whiteboards, screen sharing, or presentations, providing better visualization and annotation capabilities. This is particularly important for training, speeches, or educational video conferences.

 

Virtual Backgrounds and Filters

Virtual background and filter technologies allow users to customize the appearance of their videos. AI can be used to recognize and apply virtual backgrounds in real-time, and filters can be added to enhance video quality, allowing users to present different visual effects in various settings.

 

Multi-Camera Collaboration

In video conferences, using a single camera or multiple cameras in collaboration can capture images of multiple participants simultaneously and display them on the screen. This provides a more user-friendly and immersive experience for video conferences. In educational settings, multiple camera angles can also offer richer teaching content, increasing interactivity.

 

Nexdata Online Conference Data

 

500 People Facial Expression Data in Online Conference Scenes

500 people facial expression data in online conference scenes, including Asian, Caucasian, black, and brown multitasking, mainly young and middle-aged, collected a variety of indoor office scenes, covering meeting rooms, coffee shops, libraries , bedroom, etc., each collector collected 7 kinds of expressions: normal, happy, surprised, sad, angry, disgusted, and fearful.

 

2,000 People Human Action Data in Online Conference Scenes

2,000 people human action data in online conference scenes, including Asian, Caucasian, black, brown, mainly young and middle-aged people, collected a variety of indoor office scenes, covering meeting rooms, coffee shops, library, bedroom, etc.rnEach person collected 23 videos and 4 images. The videos included 23 postures such as opening the mouth, turning the head, closing the eyes, and touching the ears. The images included four postures such as wearing a mask and wearing sunglasses.

 

2,000 People Human Action Data in Online Conference Scenes

2,000 people human action data in online conference scenes, includes Asians, Caucasians, blacks, and browns. The age is mainly young and middle-aged. It collects a variety of indoor office scenes, covering meeting rooms, coffee shops, libraries, bedrooms, etc. Each person collected 11 videos, including human body behaviors such as shaking the body from side to side, eating, and stretching.

 

2,000 People Gesture Recognition Data in Online Conference Scenes

2,000 People Gesture Recognition Data in Meeting Scenes includes Asians, Caucasians, blacks, and browns, and the age is mainly young and middle-aged. It collects a variety of indoor office scenes, covering meeting rooms, coffee shops, libraries, bedrooms, etc. Each person collected 18 pictures and 2 videos. The pictures included 18 gestures such as clenching a fist with one hand and heart-to-heart with one hand, and the video included gestures such as clapping.