Reconstruction of fragmented trajectories of collective motion using Hadamard deep autoencoders
作者：
Highlights：
• Interactions in collective motion (CM) ensure their trajectory matrix is low-rank.
• Low-rankness of CM is the pattern learned by our Hadamard deep autoencoder (HDA).
• HDA incorporates an indicator matrix into the loss function using Hadamard product.
• HDA is trained only using the observed trajectory segments.
• Performance of HDA is validated against a low-rank matrix completion framework.
摘要
•Interactions in collective motion (CM) ensure their trajectory matrix is low-rank.•Low-rankness of CM is the pattern learned by our Hadamard deep autoencoder (HDA).•HDA incorporates an indicator matrix into the loss function using Hadamard product.•HDA is trained only using the observed trajectory segments.•Performance of HDA is validated against a low-rank matrix completion framework.
论文关键词：Multi-object tracking,Collective motion,Deep autoencoders,Hadamard product,Self-propelled particles
论文评审过程：Received 10 November 2021, Revised 20 June 2022, Accepted 6 July 2022, Available online 10 July 2022, Version of Record 20 July 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108891
