HMM-guided frame querying for bandwidth-constrained video search

12/31/2019

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by   Bhairav Chidambaram, et al.

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11

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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 omitted, without compromising frame-of-interest classification accuracy.