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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 (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200526-133907548

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Abstract

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)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2001.00057arXivDiscussion Paper
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Record Number:CaltechAUTHORS:20200526-133907548
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200526-133907548
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103460
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:26 May 2020 20:41
Last Modified:26 May 2020 20:41

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