Efficient video annotation with visual interpolation and frame selection guidance

TL;DR

This paper presents a unified framework for efficient video annotation that combines automatic frame selection and bounding box interpolation, significantly reducing manual effort and annotation time.

Contribution

It introduces a model capable of both interpolating and extrapolating bounding boxes, along with a guiding mechanism for selecting frames to annotate, advancing video annotation efficiency.

Findings

Reduces manual bounding box annotations by 60% compared to linear interpolation.

Achieves 10% faster annotation time than existing state-of-the-art methods.

Human studies show a 50% reduction in actual annotation time.

Abstract

We introduce a unified framework for generic video annotation with bounding boxes. Video annotation is a longstanding problem, as it is a tedious and time-consuming process. We tackle two important challenges of video annotation: (1) automatic temporal interpolation and extrapolation of bounding boxes provided by a human annotator on a subset of all frames, and (2) automatic selection of frames to annotate manually. Our contribution is two-fold: first, we propose a model that has both interpolating and extrapolating capabilities; second, we propose a guiding mechanism that sequentially generates suggestions for what frame to annotate next, based on the annotations made previously. We extensively evaluate our approach on several challenging datasets in simulation and demonstrate a reduction in terms of the number of manual bounding boxes drawn by 60% over linear interpolation and by 35% over an off-the-shelf tracker. Moreover, we also show 10% annotation time improvement over a state-of-the-art method for video annotation with bounding boxes [25]. Finally, we run human annotation experiments and provide extensive analysis of the results, showing that our approach reduces actual measured annotation time by 50% compared to commonly used linear interpolation.