Proposals from binary tree and spatio-temporal tunnel for temporal segmentation of rough videos

Existing temporal segmentation methods suffer from the problems of high computational complexity and complicated steps. To address this issue, we present a method that combines the binary tree and spatio-temporal tunnel (STT) for temporal segmentation of rough videos. First, we compute initial cumulative spatio-temporal flow to determine flow overflow of sub-video which is divided from a rough video. Second, the decision tree is generated by combining binary tree and balance factor to dynamically adjust the sampling line of the STT. Finally, pixels on the sampling line are extracted to generate an adaptive STT for temporal proposals. Experimental results show that the computational complexity of the proposed method is significantly better than that of the comparison methods while ensuring accuracy.