A new method for interaction recognition based on sparse representation of feature covariance matrices was presented.Firstly,the dense trajectories(DT)extracted from the video were clustered into different groups to e...A new method for interaction recognition based on sparse representation of feature covariance matrices was presented.Firstly,the dense trajectories(DT)extracted from the video were clustered into different groups to eliminate the irrelevant trajectories,which could greatly reduce the noise influence on feature extraction.Then,the trajectory tunnels were characterized by means of feature covariance matrices.In this way,the discriminative descriptors could be extracted,which was also an effective solution to the problem that the description of the feature second-order statistics is insufficient.After that,an over-complete dictionary was learned with the descriptors and all the descriptors were encoded using sparse coding(SC).Classification was achieved using multiple instance learning(MIL),which was more suitable for complex environments.The proposed method was tested and evaluated on the WEB Interaction dataset and the UT interaction dataset.The experimental results demonstrated the superior efficiency.展开更多
基金Project(51678075) supported by the National Natural Science Foundation of ChinaProject(2017GK2271) supported by the Science and Technology Project of Hunan Province,China
文摘A new method for interaction recognition based on sparse representation of feature covariance matrices was presented.Firstly,the dense trajectories(DT)extracted from the video were clustered into different groups to eliminate the irrelevant trajectories,which could greatly reduce the noise influence on feature extraction.Then,the trajectory tunnels were characterized by means of feature covariance matrices.In this way,the discriminative descriptors could be extracted,which was also an effective solution to the problem that the description of the feature second-order statistics is insufficient.After that,an over-complete dictionary was learned with the descriptors and all the descriptors were encoded using sparse coding(SC).Classification was achieved using multiple instance learning(MIL),which was more suitable for complex environments.The proposed method was tested and evaluated on the WEB Interaction dataset and the UT interaction dataset.The experimental results demonstrated the superior efficiency.
