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HMM-guided frame querying for bandwidth-constrained video search

Chidambaram, Bhairav and McGill, Mason and Perona, Pietro (2019) HMM-guided frame querying for bandwidth-constrained video search. . (Unpublished)

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We design an agent to search for frames of interest in video stored on a remote server, under bandwidth constraints. Using a convolutional neural network to score individual frames and a hidden Markov model to propagate predictions across frames, our agent accurately identifies temporal regions of interest based on sparse, strategically sampled frames. On a subset of the ImageNet-VID dataset, we demonstrate that using a hidden Markov model to interpolate between frame scores allows requests of 98% of frames to be omitted, without compromising frame-of-interest classification accuracy.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Perona, Pietro0000-0002-7583-5809
Record Number:CaltechAUTHORS:20200526-133907548
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103460
Deposited By: Tony Diaz
Deposited On:26 May 2020 20:41
Last Modified:26 May 2020 20:41

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