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Human activity prediction using saliency-aware motion enhancement and weighted LSTM network
2021
EURASIP Journal on Image and Video Processing
AbstractIn recent years, great progress has been made in recognizing human activities in complete image sequences. However, predicting human activity earlier in a video is still a challenging task. In this paper, a novel framework named weighted long short-term memory network (WLSTM) with saliency-aware motion enhancement (SME) is proposed for video activity prediction. First, a boundary-prior based motion segmentation method is introduced to use shortest geodesic distance in an undirected
doi:10.1186/s13640-020-00544-0
fatcat:kuhulehob5e7zcsiwyb4gkwk3e