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arXiv · 2110.01015

Spatio-Temporal Video Representation Learning for AI Based Video Playback Style Prediction

Rishubh Parihar·Gaurav Ramola·Ranajit Saha·Ravi Kini·Aniket Rege·Sudha Velusamy
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Abstract

Ever-increasing smartphone-generated video content demands intelligent techniques to edit and enhance videos on power-constrained devices. Most of the best performing algorithms for video understanding tasks like action recognition, localization, etc., rely heavily on rich spatio-temporal representations to make accurate predictions. For effective learning of the spatio-temporal representation, it is crucial to understand the underlying object motion patterns present in the video. In this paper, we propose a novel approach for understanding object motions via motion type classification. The proposed motion type classifier predicts a motion type for the video based on the trajectories of the objects present. Our classifier assigns a motion type for the given video from the following five primitive motion classes: linear, projectile, oscillatory, local and random. We demonstrate that the representations learned from the motion type classification generalizes well for the challenging downstream task of video retrieval. Further, we proposed a recommendation system for video playback style based on the motion type classifier predictions.

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Rishubh Parihar, Gaurav Ramola, Ranajit Saha, Ravi Kini, Aniket Rege, Sudha Velusamy. 2021-10-03. Spatio-Temporal Video Representation Learning for AI Based Video Playback Style Prediction. https://arxiv.org/abs/2110.01015

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