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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.
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.
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