Encode the Unseen: Predictive Video Hashing for Scalable Mid-Stream Retrieval

TL;DR

This paper introduces a novel predictive hashing framework for mid-stream video retrieval, enabling real-time search of streaming videos by inferring unseen future content, with significant performance improvements demonstrated on benchmark datasets.

Contribution

It presents the first hashing method capable of predicting unseen future video content for mid-stream retrieval, addressing a new challenge in real-time video search.

Findings

Achieved up to 7.4% mAP@20 improvement on FCVID.

Demonstrated feasibility of predicting unseen video content.

Outperformed adapted baseline methods in streaming scenarios.

Abstract

This paper tackles a new problem in computer vision: mid-stream video-to-video retrieval. This task, which consists in searching a database for content similar to a video right as it is playing, e.g. from a live stream, exhibits challenging characteristics. Only the beginning part of the video is available as query and new frames are constantly added as the video plays out. To perform retrieval in this demanding situation, we propose an approach based on a binary encoder that is both predictive and incremental in order to (1) account for the missing video content at query time and (2) keep up with repeated, continuously evolving queries throughout the streaming. In particular, we present the first hashing framework that infers the unseen future content of a currently playing video. Experiments on FCVID and ActivityNet demonstrate the feasibility of this task. Our approach also yields a significant mAP@20 performance increase compared to a baseline adapted from the literature for this task, for instance 7.4% (2.6%) increase at 20% (50%) of elapsed runtime on FCVID using bitcodes of size 192 bits.