# Compact CNN for Indexing Egocentric Videos

arXiv: https://arxiv.org/abs/1504.07469 · HTML: https://mlsanity.com/paper/1504.07469/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2015.

## Built on by (most cited first, 9 of 9)

- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [Self-Regulated Learning for Egocentric Video Activity Anticipation](https://mlsanity.com/paper/2111.11631/) (2021)
- [ST-Think: How Multimodal Large Language Models Reason About 4D Worlds from Ego-Centric Videos](https://mlsanity.com/paper/2503.12542/) (2025)
- [Generative Adversarial Network for Future Hand Segmentation from Egocentric Video](https://mlsanity.com/paper/2203.11305/) (2022)
- [The Invisible EgoHand: 3D Hand Forecasting through EgoBody Pose Estimation](https://mlsanity.com/paper/2504.08654/) (2025)
- [Multimodal Cross-Domain Few-Shot Learning for Egocentric Action Recognition](https://mlsanity.com/paper/2405.19917/) (2024)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding](https://mlsanity.com/paper/2508.12687/) (2025)
- [Free-Form Composition Networks for Egocentric Action Recognition](https://mlsanity.com/paper/2307.06527/) (2023)

## Abstract (arXiv)

While egocentric video is becoming increasingly popular, browsing it is very difficult. In this paper we present a compact 3D Convolutional Neural Network (CNN) architecture for long-term activity recognition in egocentric videos. Recognizing long-term activities enables us to temporally segment (index) long and unstructured egocentric videos. Existing methods for this task are based on hand tuned features derived from visible objects, location of hands, as well as optical flow. Given a sparse optical flow volume as input, our CNN classifies the camera wearer's activity. We obtain classification accuracy of 89%, which outperforms the current state-of-the-art by 19%. Additional evaluation is performed on an extended egocentric video dataset, classifying twice the amount of categories than current state-of-the-art. Furthermore, our CNN is able to recognize whether a video is egocentric or not with 99.2% accuracy, up by 24% from current state-of-the-art. To better understand what the network actually learns, we propose a novel visualization of CNN kernels as flow fields.