基于滑动窗口和动态规划的连续动作分割与识别
|
摘要
针对现有动作识别中对连续动作识别研究较少且单一算法对连续动作识别效果较差的问题,提出在单个动作建模的基础上,采用滑动窗口法和动态规划法结合,实现连续动作的分割与识别。首先,采用深度置信网络和隐马尔可夫结合的模型DBN-HMM对单个动作建模;其次,运用所训练动作模型的对数似然值和滑动窗口法对连续动作进行评分估计,实现初始分割点的检测;然后,采用动态规划对分割点位置进行优化并对单个动作进行识别。在公开动作数据库MSR Action3D上进行连续动作分割与识别测试,结果表明基于滑动窗口的动态规划能够优化分割点的选取,进而提高识别精度,能够用于连续动作识别。
Concerning the fact that there are few researches on continuous action recognition in the field of action recognition and single algorithms have poor effect on continuous action recognition,a segmentation and recognition method of continuous actions was proposed based on single motion modeling by combining sliding window method and dynamic programming method.Firstly,the single action model was constructed based on the Deep Belief Network and Hidden Markov Model(DBN-HMM).Secondly,the logarithmic likelihood value of the trained action model and the sliding window method were used to estimate the score of the continous action,detecting the initial segmentation points.Thirdly,the dynamic programming method was used to optimize the location of the segmentation points and identify the single action.Finally,the testing experiments of continuous action segmentation and recognition were conducted with an open action database MSR Action3D.The experimental results show that the dynamic programming based on sliding window can optimize the selection of segmentation points to improve the recognition accuracy,which can be used to recognize continuous action.
Concerning the fact that there are few researches on continuous action recognition in the field of action recognition and single algorithms have poor effect on continuous action recognition,a segmentation and recognition method of continuous actions was proposed based on single motion modeling by combining sliding window method and dynamic programming method.Firstly,the single action model was constructed based on the Deep Belief Network and Hidden Markov Model(DBN-HMM).Secondly,the logarithmic likelihood value of the trained action model and the sliding window method were used to estimate the score of the continous action,detecting the initial segmentation points.Thirdly,the dynamic programming method was used to optimize the location of the segmentation points and identify the single action.Finally,the testing experiments of continuous action segmentation and recognition were conducted with an open action database MSR Action3D.The experimental results show that the dynamic programming based on sliding window can optimize the selection of segmentation points to improve the recognition accuracy,which can be used to recognize continuous action.
引文
[1]胡琼,秦磊,黄庆明.基于视觉的人体动作识别综述[J].计算机学报,2013,36(12):2512-2524.(HU Q,QIN L,HUANG QM.A survey on visual human action recognition[J].Chinese Journal of Computers,2013,36(12):2512-2524.)
[2]AGGARWAL J K,RYOO M S.Human activity analysis:a review[J].ACM Computing Surveys,2011,43(3):Article No.16.
[3]KOPPULA H S,SAXENA A.Anticipating human activities using object affordances for reactive robotic response[J].IEEE Transactions on Pattern analysis and Machine Intelligence,2015,38(1):1-14.
[4]ZHANG C,TIAN Y.RGB-D camera-based daily living activity recognition[J].Journal of Computer Vision and Image Processing,2012,2(4):1-7.
[5]白栋天,张磊,黄华.RGB-D视频中连续动作识别[J].中国科技论文,2016(2):168-172.(BAI D T,ZHANG L,HUANG H.Recognition continuous human actions from RGB-D videos[J].China Science Paper,2016(2):168-172.)
[6]DARRELL T,PENTLAND A.Space-time gestures[C]//Proceedings of the 1993 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,1993:335-340.
[7]OKA R.Spotting method for classification of real world data[J].Computer Journal,1998,41(8):559-565.
[8]GONG D,MEDIONI G,ZHAO X.Structured time series analysis for human action segmentation and recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(7):1414-1427.
[9]ZHU G,ZHANG L,SHEN P,et al.An online continuous human action recognition algorithm based on the Kinect sensor[J].Sensors,2016,16(2):161-179.
[10]LEI J,LI G,ZHANG J,et al.Continuous action segmentation and recognition using hybrid convolutional neural network-hidden Markov model model[J].IET Computer Vision,2016,10(6):537-544.
[11]KULKARNI K,EVANGELIDIS G,CECH J,et al.Continuous action recognition based on sequence alignment[J].International Journal of Computer Vision,2015,112(1):90-114.
[12]EVANGELIDIS G D,SINGH G,HORAUD R.Continuous gesture recognition from articulated poses[C]//Proceedings of the 2014European Conference on Computer Vision.Cham:Springer,2014:595-607.
[13]SONG Y,GU Y,WANG P,et al.A Kinect based gesture recognition algorithm using GMM and HMM[C]//Proceedings of the2013 6th International Conference on Biomedical Engineering and Informatics.Piscataway,NJ:IEEE,2013:750-754.
[14]VITERBI A J.Error bounds for convolutional codes and an asymptotically optimum decoding algorithm[J].IEEE Transactions on Information Theory,1967,13(2):260-269.
[15]TAYLOR G W,HINTON G E,ROWEIS S.Modeling human motion using binary latent variables[C]//Proceedings of the 19th International Conference on Neural Information Processing Systems.Cambridge,MA:MIT Press,2007:1345-1352.
[16]HINTON G E,SIMON O,TEH Y W,et al.A fast learning algorithm for deep belief nets[J].Neural Computation,2014,18(7):1527-1554.
[17]LI W,ZHANG Z,LIU Z.Action recognition based on a bag of 3Dpoints[C]//Proceedings of the 2010 IEEE Computer Vision and Pattern Recognition Workshops.Washington,DC:IEEE Computer Society,2010:9-14.
[2]AGGARWAL J K,RYOO M S.Human activity analysis:a review[J].ACM Computing Surveys,2011,43(3):Article No.16.
[3]KOPPULA H S,SAXENA A.Anticipating human activities using object affordances for reactive robotic response[J].IEEE Transactions on Pattern analysis and Machine Intelligence,2015,38(1):1-14.
[4]ZHANG C,TIAN Y.RGB-D camera-based daily living activity recognition[J].Journal of Computer Vision and Image Processing,2012,2(4):1-7.
[5]白栋天,张磊,黄华.RGB-D视频中连续动作识别[J].中国科技论文,2016(2):168-172.(BAI D T,ZHANG L,HUANG H.Recognition continuous human actions from RGB-D videos[J].China Science Paper,2016(2):168-172.)
[6]DARRELL T,PENTLAND A.Space-time gestures[C]//Proceedings of the 1993 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,1993:335-340.
[7]OKA R.Spotting method for classification of real world data[J].Computer Journal,1998,41(8):559-565.
[8]GONG D,MEDIONI G,ZHAO X.Structured time series analysis for human action segmentation and recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(7):1414-1427.
[9]ZHU G,ZHANG L,SHEN P,et al.An online continuous human action recognition algorithm based on the Kinect sensor[J].Sensors,2016,16(2):161-179.
[10]LEI J,LI G,ZHANG J,et al.Continuous action segmentation and recognition using hybrid convolutional neural network-hidden Markov model model[J].IET Computer Vision,2016,10(6):537-544.
[11]KULKARNI K,EVANGELIDIS G,CECH J,et al.Continuous action recognition based on sequence alignment[J].International Journal of Computer Vision,2015,112(1):90-114.
[12]EVANGELIDIS G D,SINGH G,HORAUD R.Continuous gesture recognition from articulated poses[C]//Proceedings of the 2014European Conference on Computer Vision.Cham:Springer,2014:595-607.
[13]SONG Y,GU Y,WANG P,et al.A Kinect based gesture recognition algorithm using GMM and HMM[C]//Proceedings of the2013 6th International Conference on Biomedical Engineering and Informatics.Piscataway,NJ:IEEE,2013:750-754.
[14]VITERBI A J.Error bounds for convolutional codes and an asymptotically optimum decoding algorithm[J].IEEE Transactions on Information Theory,1967,13(2):260-269.
[15]TAYLOR G W,HINTON G E,ROWEIS S.Modeling human motion using binary latent variables[C]//Proceedings of the 19th International Conference on Neural Information Processing Systems.Cambridge,MA:MIT Press,2007:1345-1352.
[16]HINTON G E,SIMON O,TEH Y W,et al.A fast learning algorithm for deep belief nets[J].Neural Computation,2014,18(7):1527-1554.
[17]LI W,ZHANG Z,LIU Z.Action recognition based on a bag of 3Dpoints[C]//Proceedings of the 2010 IEEE Computer Vision and Pattern Recognition Workshops.Washington,DC:IEEE Computer Society,2010:9-14.