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Computer vision aims at providing computers the ability to understand the content of images and videos on a high level. In an ideal system, computer vision allows machines to understand and automate tasks that the human visual system can do.
Nowadays feature-based methods are broadly used along with machine learning techniques and optimization frameworks. In recent years, Deep Learning techniques have taken the computer vision field by storm. The accuracy of deep learning algorithms on several benchmark tasks such as classification, segmentation, and optical flow has surpassed prior methods.
Human Behaviour Analysis (HBA) is more and more being of interest in Computer Vision and Artificial Intelligence researchers. Among other applications, the MMlab focuses on human-centered applications, like Video Surveillance and Ambient–Assisted Living (AAL), which have been in great demand in recent years.
Crowd Analysis and Simulation
Crowd Analysis and Simulation
Multi-view.mp4Trajectory prediction in crowded scenarios
Deep learning for crowd simulation
Video surveillance
Trajectory prediction in crowded scenarios
Trajectory prediction in crowded scenarios
Human Pose Estimation
Human Pose Estimation
Estimate the 3D poses of humans in real-time
Monocular or stereo cameras
Body pose, hands pose
Deep learning using Capsule Networks
Human mesh recovery (Computer Vision + Computer Graphics)
Ambient Assisted Living (AAL)
Ambient Assisted Living (AAL)
Precise real-time hands tracking
Track interaction with the environment
Objects detection
Body pose estimation
Gestures and activities recognition
Metrics and vitals monitoring
Stress monitoring
Tracking and detection
Patient identification
Sport applications: Football
Sport applications: Football
Real-time video stitching
Automatic tactical camera
Automatic Highlight Generation
Automatic Highlight Generation
Automatic video analysis for finding important events to create an highlight video
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