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Unsupervised Spatio-temporal Latent Feature Clustering for Multiple-object Tracking and Segmentation
[article]
2021
arXiv
pre-print
Assigning consistent temporal identifiers to multiple moving objects in a video sequence is a challenging problem. A solution to that problem would have immediate ramifications in multiple object tracking and segmentation problems. We propose a strategy that treats the temporal identification task as a spatio-temporal clustering problem. We propose an unsupervised learning approach using a convolutional and fully connected autoencoder, which we call deep heterogeneous autoencoder, to learn
arXiv:2007.07175v3
fatcat:u7kcljkcafhtrlwdekqemmesge