计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
3期
157-161
,共5页
Kinect%骨架信息%动态时间规整%K-最近邻%手势识别%交通警察手势
Kinect%骨架信息%動態時間規整%K-最近鄰%手勢識彆%交通警察手勢
Kinect%골가신식%동태시간규정%K-최근린%수세식별%교통경찰수세
Kinect%skeleton data%dynamic time warping%K-nearest neighbor%gesture recognition%traffic cop gesture
针对现有的手势识别算法识别率低、鲁棒性弱的问题，提出一种基于Kinect骨架信息的交通警察手势识别方法。从Kinect深度图像中预测人体骨架节点的坐标位置，将节点的运动轨迹作为训练和测试的特征，结合距离加权动态时间规整算法和K-最近邻分类器进行识别。实验表明，在参数最优的情况下，该方法对八种交通警察手势的平均识别率达到98.5%，可应用于智能交通等领域。
針對現有的手勢識彆算法識彆率低、魯棒性弱的問題，提齣一種基于Kinect骨架信息的交通警察手勢識彆方法。從Kinect深度圖像中預測人體骨架節點的坐標位置，將節點的運動軌跡作為訓練和測試的特徵，結閤距離加權動態時間規整算法和K-最近鄰分類器進行識彆。實驗錶明，在參數最優的情況下，該方法對八種交通警察手勢的平均識彆率達到98.5%，可應用于智能交通等領域。
침대현유적수세식별산법식별솔저、로봉성약적문제，제출일충기우Kinect골가신식적교통경찰수세식별방법。종Kinect심도도상중예측인체골가절점적좌표위치，장절점적운동궤적작위훈련화측시적특정，결합거리가권동태시간규정산법화K-최근린분류기진행식별。실험표명，재삼수최우적정황하，해방법대팔충교통경찰수세적평균식별솔체도98.5%，가응용우지능교통등영역。
To overcome the problems such as low recognition rate and weak robustness of current gesture recognition algorithms, this paper presents a novel traffic gesture recognition method based on Kinect skeleton data. It predicts skeleton joints’coordinates from depth image captured by Kinect sensor. Then it uses joints’trajectories as the features of training and testing. Distance weighting dynamic time warping algorithm and K-nearest neighbor algorithm are used to recognize the giving sample. The experimental results show that when argument is optimal, the average recognition rate is up to 98.5%tested on eight traffic cop gestures, so this method can be applied to intelligent traffic field.
