Recent Developments in Human Motion Analysis

Liang Wang, Weiming Hu, Tieniu Tan

Chinese Academy of Sciences, Beijing,

People’s Republic of China

2002 Pattern Recognition

Outline

• Potential application• Detection• Tracking• Behavior analysis• Future researches

Potential Applications

• Visual surveillance• Tracking and recognition techniques of face

and gait• Advanced user interface

• Control and command by speech, gestures, body poses, facial expressions, etc.

• Motion-based diagnosis and identification• Medical diagnosis, sports, orthopedic patient

s, choreography

Motion Detection

• Human detection aims at segmenting regions corresponding to people from the rest of an image.

• Motion segmentation• Background subtraction• Statistical methods• Temporal differencing• Optical flow

Motion Detection

• Object classification• The purpose of moving object classification is to pr

ecisely extract the region corresponding to people from all moving blobs obtained by the motion segmentation methods.

• Shape-based• NN classifier

• Motion-based• Periodic property• Residual flow

Human Tracking

• Useful mathematical tools• Kalman filter• Condensation algorithm• Dynamic Bayesian network

• Different classification• Hand, face, leg, whole body• Single-view, multiple-view, omni-directional view• 2-D, 3-D• Indoors, outdoors• Single human, multiple human, human groups• Moving, stationary• Monocular, stereo

Human Tracking

• Model-based• Stick figure (fig.)• 2-D contour (fig.)• Volumetric models (fig.)

• Region-based (fig.)• Active-contour-based (fig.)• Feature-based

Recognition and Description of Human Activities• Behavior understanding is to analyze an

d recognize human motion patterns, and to produce high-level description of actions and interactions.

• General techniques• Dynamic time warping (DTW)• Hidden Markov models (HMMs)• Neural network (NN)

Recognition and Description of Human Activities• Action recognition

• Template matching• State-space approaches

• Semantic description

Further Researches

1) Segmentation

2) Occlusion handling

3) 3-D modeling and tracking

4) Use of multiple cameras

5) Action understanding

6) Performance evaluation