By David A. Forsyth, Okan Arikan, Leslie Ikemoto
Computational experiences of Human movement: half 1, monitoring and movement Synthesis stories equipment for kinematic monitoring of the human physique in video. The overview confines itself to the sooner phases of movement, concentrating on monitoring and movement synthesis. there's an in depth dialogue of open matters. The authors determine a few perplexing phenomena linked to the alternative of human movement illustration --- joint angles vs. joint positions. The assessment concludes with a brief advisor to assets and an intensive bibliography of over four hundred references. Computational experiences of Human movement: half 1, monitoring and movement Synthesis is a useful reference for these engaged in computational geometry, special effects, picture processing, imaging regularly, and robot.
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Additional resources for Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis (Foundations and Trends in Computer Graphics and Vision)
Computer Vision and Pattern Recognition, 2000, c 2000 IEEE. translation in depth (which is important and often forgotten) and a two-fold ambiguity at each segment. For the moment, assume we know all segment lengths and the camera scale. We can now reconstruct the body by obtaining a reconstruction for each segment, and joining them up. Each segment has a single missing degree of freedom (depth), but the segments must join up, meaning that we have a discrete set of ambiguities. Depending on circumstances, one might work with from nine to eleven body segments (the head is often omitted; the torso can reasonably be modelled with several segments), yielding from 512 to 2048 possible reconstructions.
Current likelihood models compare some set of predicted with observed image features (typically, silhouette edges), and so must have multiple peaks corresponding to the ambiguities described. 5). While this makes the multiple 42 Tracking: Relations between 3D and 2D Fig. 5 Several nasty phenomena result from kinematic ambiguities and from kinematic limits, as Sminchisescu and Triggs [359, 362]. Ambiguities in reconstruction – which are caused because body segments can be oriented in 3D either toward or away from the camera, as described in the text – result in multiple modes in the posterior.
Fit: Fit the best value of ∆θ to the flow between the frame n and frame n + 1 using the flow model given by the derivative evaluated at θn . • Update: Update the parameters by θn+1 = θn + ∆θ and set n to n + 1. This should be seen as a primitive integrator, using Euler’s method and inheriting all the problems that come with it. This view confirms the reasonable suspicion that fast movements are unlikely to be tracked well unless that sampling rate is high. 2 The flow model from the chain rule In the special case of segments lying on a kinematic tree – a series of links attached by joints of known parametric form, where there are no loops – the chain rule means that the derivative takes a special form.
Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis (Foundations and Trends in Computer Graphics and Vision) by David A. Forsyth, Okan Arikan, Leslie Ikemoto