Date post: | 19-Dec-2015 |
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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