Material Recognition Using Enhanced Cloth Motion Dense Trajectory

In order to overcome the low recognition rate of different cloth materials due to neglecting the influence of the dynamic features of simulation video,this paper presents a novel cloth material recognition method using the enhanced cloth motion dense trajectory features.Firstly,a material synthesis method is presented to construct the simulation video database with 64 kinds of cloth materials.Then,the feature information of each cloth material video is enhanced and the non-dynamic features are eliminated by transferring the pre-trained VGG network.Secondly,in order to capture and represent the dynamic features of the cloth simulation videos with different materials,the novel cloth motion dense trajectory feature is calculated.Finally,the feature database of cloth dynamic information can be created by coding their fisher vectors,and the SVM classifier can also be trained to set up the mapping of dynamic information of cloth motion video to material attribute parameters.Experimental results show that the recognition rate for 64 kinds of different cloth materials is 73.83%by using our constructed cloth simulation video dataset.