鏂規硶錛?Check height of the foot
浼樼偣錛歟asy
緙虹偣錛?easily fooled錛宨f a character skids to a stop
Baseline mehtod 2
鏂規硶錛?Check speed of the foot
浼樼偣錛歟asy
緙虹偣錛?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
鏂規硶錛?detect zero-crossing in acceration space of the end effectors
浼樼偣錛?work well for non-noisy data
緙虹偣錛?nbsp; 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
鏂規硶錛歞etect frames in which the feet are stationary
浼樼偣錛歸ork well for non-noisy data
緙虹偣錛?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
棰樼洰錛欶ootskate cleanup for motion capture editing
鏂規硶錛歶se specific thresholds on the position and velocity of the feet to detect them.
浼樼偣錛?/p>
緙虹偣錛歯ot reliable for motion capture animation as derivatives tend to amplify nosie in signals
Lee 2002 Siggraph
棰樼洰錛欼nteractive control of AVstars animated with human motion data
鏂規硶錛?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
浼樼偣錛?/p>
緙虹偣錛歯ot reliable for motion capture animation as derivatives tend to amplify nosie in signals
S.Menareais 2004 Symposium on Computer Animation
棰樼洰錛?Synchronization for Dynamic blending of motions
鏂規硶錛歶se specific thresholds on the position and velocity of the feet to detect them
浼樼偣錛?/p>
緙虹偣 not reliable for motion capture animation as derivatives tend to amplify nosie in signals
Ikemoto 06 Symposium on Interactive 3D Graphics
鏂規硶錛歶se 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.
浼樼偣錛?semi-automatic(璁粌閮ㄥ垎闇瑕佹墜鍔ㄥ弬涓?,
緙虹偣錛?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.
鎯蟲硶錛氳繖涓柟娉曟病鐪嬫噦銆傘傘傝瀹炶瘽銆傘傦紙涓涓嬪崍閮藉湪鎼炶繖涓傘傚嚭浜嗛厤浜嗕釜Emacs銆傘傘傦級
1錛?棣栧厛鎬庝箞鎶婁笁緇磎ark鐐圭殑杞ㄨ抗鏄犲皠鍒頒簩緇翠笂錛岃屼笖閮芥槸瀵歸綈鐨勶紵 浠巖oot鐐規潵鎼烇紵錛堣矊浼紃oot鐐圭殑紜彲浠ユ悶錛?/p>
2錛?涓嬮潰灝卞墿涓浜涚粏鑺傜殑涓滆タ銆傘?1甯х殑闂銆傘傘?/p>
璨屼技鐨勭‘鏄疭KELETON鐩稿叧鐨勩傘傛墍浠ヤ笉閫傚悎鎴戜滑鐨勯棶棰樸傘傘俿ummer璇寸殑鐨勭‘鏄笉閿欑殑銆傘?/p>
Le 06 Symposium on Computer Animation
棰樼洰錛歊obust kinematic constraint detection for motion data
鏂規硶錛?/p>
浼樼偣錛?/p>
緙虹偣錛?/p>
榪欎釜Roubust Kinematic 鐪嬪緱鎴戠湡鏄ご澶х殑寰堝晩銆傘傘係VD鍒嗚В錛岀嚎鎬т唬鏁般傘傘傛槧灝勭┖闂淬傘傘傞珮鏂櫔澹般傘傘傚櫔澹版ā鏉褲傘傘傛垜鍕掍釜鍘匯傘傚厛琛ュ熀紜銆傘?/p>
棣栧厛浠ョ敤Abstract鐨勪竴鍙ヨ瘽 Spatial Proximities of end-effectors with tagged objects during zero-crossing in acceleration space are used to isolate significant events and abstract constraints from an agents`s action
絀洪棿鐨勮窛紱葷害鏉熺殑鐩殑灝辨槸涓轟簡鎶藉幓鍑哄叿鏈夋槑鏄炬剰涔夌殑甯?/p>
The zero-crossing point in trajectory implies changes in motion such as starting from rest ,coming to a stop ,or changing the velocity direction .
浠ユ姄鍙栬瀛愪負渚嬶紝棣栧厛鍦ㄦ墜涓婃湁涓涓猰ark鐐癸紝鏉瓙涓婃湁涓涓猼ag鐐廣?/p>
褰撲簩鑰呬箣闂寸殑璺濈灝忎簬涓瀹氬肩殑鏃跺欙紝寮濮嬭繘琛岃窡韙傜劧鍚庡湪鍔犻熷害絀洪棿涓繘琛孼ero Crossing
絎竴涓浘鏄窛紱葷害鏉燂紝絎簩涓浘鍔犻熷害綰︽潫錛屽叾涓粍鑹茬殑浠ｈ〃妯悜鍔犻熷害錛屾柟鍚戝鍙沖浘鎵紺恒傝繘琛岃繃闆舵嫻嬪氨鏄湪Acceleration絀洪棿涓繘琛岀殑銆傘備絾涓嶆竻妤氳繖涓粨鏋滄庝箞鏍楓傘?/p>
鎰熻榪欎釜瀹為獙灝辨槸媯嫻嬪姞閫熷害鍙樺寲錛屼絾濂椾笂浜嗕竴涓猌eroCrossing鐨勫附瀛愩傘?/p>
涓庢垜浠疄楠岀殑鍖哄埆:
1 棣栧厛榪欎釜鍔犻熷害鏄竴涓簩緇寸殑銆傘備漢浣撶殑Gait Analysis鏄竴涓?緇寸殑榪囩▼錛屼絾鍙互鍐嶅墠榪涙柟鍚戜笂榪涜鎶曞獎銆傘備粠鑰岃揪鍒頒簩緇磋姹傘?/p>
2 鎴戜滑鐨凣ait Analysis涓病鏈塼ag鐐廣傘傚鏋滆鏈夛紝涔熷彧鑳芥槸鍦伴潰綆椾綔鏄竴涓猼ag鐐廣傘傞粍姝﹀笀鍏勮錛岃繖鍏朵腑鏈変竴涓湴闈㈡爣瀹氱殑榪囩▼銆傘傝繖涓殑紜渶瑕併傘?/p>
榪橀渶瑕佸皢3D鏁版嵁錛屼粠鏍囧畾鍧愭爣緋諱腑杞崲鍒板湴闈㈠潗鏍囩郴涓紝鍥犱負鎴戜滑浣跨敤鐨勭害鏉熸潯浠墮兘鏄湴闈㈠潗鏍囩郴涓殑銆傘?/p>
3 榪欎釜瀹為獙瀹炵幇璧鋒潵姣旇緝鏂逛究銆傘?/p>
Motion analysis data provides large amounts of dta to describe motion such as walking speed and gait events, as well as joint angles,forces, and moments as function of the percent of the gait cycle. Using those data joint kinetics ,joint moments and joint powers have been used to for gait recognition lately .
On the other hand Biometrix gait analysis concentrates on individual`s gait recognition in a variety of eifferent areas and scenarios .As a resultof this ,biometric gait analysis is based on visual data capture and analysis systems.
Due to lavk of information like motion analysis system biometric gait recognition users computer vision method to describe motion that is exclusively being applied to identification tasks. However, gait can disclose more that identity.
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