Baseline method1
方法： Check height of the foot
优点：easy
缺点： easily fooled，if a character skids to a stop
Baseline mehtod 2
方法： Check speed of the foot
优点：easy
缺点： unreliable the markers have some speed even during foot plants. marker data is noisy
Bindiganavale 98 Proceeding of the International Workshop on Modeling and Motion Capture Techniques for Virtual Environments
方法： detect zero-crossing in acceration space of the end effectors
优点： work well for non-noisy data
缺点： unreliable on motion capture data not reliabel when working with noisy signals require manualy tagged-objects to avoid checking for collison with all the objects in the scene
Liu and Popovic 2002 Siggraph
方法：detect frames in which the feet are stationary
优点：work well for non-noisy data
缺点： unreliable on motion capture data, not automatic .This method is dedicated to keyframed animation and is not intended to be applied to motion capture as it does not consider noise in the data.
Kovar 2002 Symposium on Computer Animation
题目：Footskate cleanup for motion capture editing
方法：use specific thresholds on the position and velocity of the feet to detect them.
优点：
缺点：not reliable for motion capture animation as derivatives tend to amplify nosie in signals
Lee 2002 Siggraph
题目：Interactive control of AVstars animated with human motion data
方法： consider body segments and objects in the environment relative velocity and position to decide whether a body segment is in contact with an object in the scene or not
优点：
缺点：not reliable for motion capture animation as derivatives tend to amplify nosie in signals
S.Menareais 2004 Symposium on Computer Animation
题目： Synchronization for Dynamic blending of motions
方法：use specific thresholds on the position and velocity of the feet to detect them
优点：
缺点 not reliable for motion capture animation as derivatives tend to amplify nosie in signals
Ikemoto 06 Symposium on Interactive 3D Graphics
方法：use a classifier to detect when foot plants should occur.By labeling a small set of frames, a user trains a classifier to detect when the foot should be planted.The classifier then automatically labels the remainder of the frames.
优点： semi-automatic(训练部分需要手动参与),
缺点： This method is dedicated to footplants detection and would be difficult to generilized to any kind of effectors and /or constraints .Indeed ,detecting another type of constraints would require to build a new kind of teature vectors and to train the calssifier once more.
想法：这个方法没看懂。。。说实话。。（一下午都在搞这个。。出了配了个Emacs。。。）
1） 首先怎么把三维mark点的轨迹映射到二维上，而且都是对齐的？ 从root点来搞？（貌似root点的确可以搞）
2） 下面就剩一些细节的东西。。21帧的问题。。。
貌似的确是SKELETON相关的。。所以不适合我们的问题。。。summer说的的确是不错的。。
Le 06 Symposium on Computer Animation
题目：Robust kinematic constraint detection for motion data
方法：
优点：
缺点：
这个Roubust Kinematic 看得我真是头大的很啊。。。SVD分解，线性代数。。。映射空间。。。高斯噪声。。。噪声模板。。。我勒个去。。先补基础。。