Real-time pedestrian video segmentation using memory network

Fan Zhao; Jin Liu

doi:10.1117/12.2541903

14 February 2020 Real-time pedestrian video segmentation using memory network

Fan Zhao, Jin Liu

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114320M (2020) https://doi.org/10.1117/12.2541903
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

We propose a fast and efficient method for pedestrian video segmentation. Previous methods can only use the first frame or the previous frame or a combination of the two, but in our framework, all past frames can be used by using memory network. The past frames with corresponding masks form the memory, and the current frame as the target will be segmented using the information from the memory instead of itself for only. The solution can better handle the problems such as movement and appearance changes in the video. ResUnet is used as the segmentation network to improve time efficiency. Since no dataset is publicly available yet for pedestrian video segmentation, we have internally labeled a large dataset which contains 216 sequences in the training set and 24 sequences in the test set and it will be made public in the future. We validate our method on the test set and achieved the mean IU of 92.6 which is better than using previous methods while keeping real-time(90FPS for input of 160*96 on a TITAN V).

Citation Download Citation

Fan Zhao and Jin Liu "Real-time pedestrian video segmentation using memory network", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320M (14 February 2020); https://doi.org/10.1117/12.2541903

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