HMM-guided frame querying for bandwidth-constrained video search

• URL: http://arxiv.org/abs/2001.00057v1
• Date: Tue, 31 Dec 2019 19:54:35 GMT
• Title: HMM-guided frame querying for bandwidth-constrained video search
• Authors: Bhairav Chidambaram, Mason McGill, Pietro Perona
• Abstract summary: 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.
• Score: 16.956238550063365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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