By Jun Ohya, Akira Utsumi, Junji Yamato
Analyzing Video Sequences of a number of people: monitoring, Posture Estimation and behaviour Recognition describes a few laptop vision-based equipment that research video sequences of people. extra particularly, tools for monitoring a number of people in a scene, estimating postures of a human physique in 3D in real-time, and spotting a person's habit (gestures or actions) are mentioned. For the monitoring set of rules, the authors built a non-synchronous procedure that tracks a number of folks through exploiting a Kalman filter out that's utilized to a number of video sequences. For estimating postures, an set of rules is gifted that locates the numerous issues which be certain postures of a human physique, in 3D in real-time. Human actions are well-known from a video series via the HMM (Hidden Markov Models)-based procedure that the authors pioneered. The effectiveness of the 3 tools is proven through experimental results.
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Additional info for Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation and Behavior Recognition
1991b]; unfortunately, this strategy requires range images, which cannot easily be acquired in many realistic applications. , 1991]. For this, some reference points on the human are given manually. , 1993]; the contour based matching used in this method is sensitive to image noise, and accurate parameter estimation for arbitrary postures is a difficult task for monocular image analysis. , 2000]. Although these methods can deal with 3-D human movements, they are not easily applied to arbitrary human postures, because they require constraints and knowledge.
For this, some reference points on the human are given manually. , 1993]; the contour based matching used in this method is sensitive to image noise, and accurate parameter estimation for arbitrary postures is a difficult task for monocular image analysis. , 2000]. Although these methods can deal with 3-D human movements, they are not easily applied to arbitrary human postures, because they require constraints and knowledge. Moreover, in some of these methods, manual fitting of a human model to some frames is used.
For ho and hI. observation node m could obtain the estimated observation positions based on past observations for each from the tracking node. 1, Tracking algorithm), the estimations of the observation positions are also given as Gaussian distributions (in one dimension in this case). The matching between the image features and tracking models is based on the Mahalanobis distance between them [Cox, 1993]. If some unmatched image features remain after the process, they must belong to a new object.