# HMM-guided frame querying for bandwidth-constrained video search

Source: [HMM-guided frame querying for bandwidth-constrained video search](<https://www.sota2.com/research/paper/68fa66e61e209131cea13039>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:01:21.007Z

Authors: Bhairav Chidambaram, Mason McGill, Pietro Perona

Published: 2019-12-31T19:54:35.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.00057>)

[PDF](<https://arxiv.org/pdf/2001.00057.pdf>)