Computer Vision and Behavior Analysis

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

  • Trajectory prediction in crowded scenarios

  • Deep learning for crowd simulation

  • Video surveillance

Trajectory prediction in crowded scenarios

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)

  • 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

  • Real-time video stitching

  • Automatic tactical camera

Automatic Highlight Generation

  • Automatic video analysis for finding important events to create an highlight video