Analyzing Video Sequences of Multiple Humans: Tracking, Posture
Estimation and Behavior Recognition describes some computer
vision-based methods that analyze video sequences of humans. More
specifically, methods for tracking multiple humans in a scene,
estimating postures of a human body in 3D in real-time, and recognizing
a person's behavior (gestures or activities) are discussed. For the
tracking algorithm, the authors developed a non-synchronous method that
tracks multiple persons by exploiting a Kalman filter that is applied to
multiple video sequences. For estimating postures, an algorithm is
presented that locates the significant points which determine postures
of a human body, in 3D in real-time. Human activities are recognized
from a video sequence by the HMM (Hidden Markov Models)-based method
that the authors pioneered. The effectiveness of the three methods is
shown by experimental results.