# Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder

arXiv: https://arxiv.org/abs/1801.00543 · HTML: https://mlsanity.com/paper/1801.00543/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2018.

## Built on by (most cited first, 7 of 7)

- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Unsupervised Motion Representation Learning with Capsule Autoencoders](https://mlsanity.com/paper/2110.00529/) (2021)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer](https://mlsanity.com/paper/2105.05011/) (2021)
- [Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames](https://mlsanity.com/paper/2311.17940/) (2023)
- [Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis](https://mlsanity.com/paper/2502.07277/) (2025)
- [Semantic-Guided Unsupervised Video Summarization](https://mlsanity.com/paper/2601.14773/) (2026)

## Abstract (arXiv)

Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i.e., objects of interest and their key motions) in online videos has been barely touched. In this paper, we investigate a pioneer research direction towards the fine-grained unsupervised object-level video summarization. It can be distinguished from existing pipelines in two aspects: extracting key motions of participated objects, and learning to summarize in an unsupervised and online manner. To achieve this goal, we propose a novel online motion Auto-Encoder (online motion-AE) framework that functions on the super-segmented object motion clips. Comprehensive experiments on a newly-collected surveillance dataset and public datasets have demonstrated the effectiveness of our proposed method.