# Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1608.08128 · HTML: https://mlsanity.com/paper/1608.08128/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2016.

## Built on by (most cited first, 10 of 10)

- [FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment](https://mlsanity.com/paper/2204.03646/) (2022)
- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [Graph Convolutional Module for Temporal Action Localization in Videos](https://mlsanity.com/paper/2112.00302/) (2021)
- [Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization](https://mlsanity.com/paper/2106.14118/) (2021)
- [Neuromorphic Event-based Facial Expression Recognition](https://mlsanity.com/paper/2304.06351/) (2023)
- [Action-based Contrastive Learning for Trajectory Prediction](https://mlsanity.com/paper/2207.08664/) (2022)
- [Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions](https://mlsanity.com/paper/2403.20254/) (2024)
- [Temporal Action Detection Model Compression by Progressive Block Drop](https://mlsanity.com/paper/2503.16916/) (2025)
- [Multi-Modulation Network for Audio-Visual Event Localization](https://mlsanity.com/paper/2108.11773/) (2021)
- [Object Detection During Newborn Resuscitation Activities](https://mlsanity.com/paper/2303.07790/) (2023)

## Abstract (arXiv)

This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed. As the first step, features have been extracted from video frames using an state of the art 3D Convolutional Neural Network. This features are fed in a recurrent neural network that solves the activity classification and temporally location tasks in a simple and flexible way. Different architectures and configurations have been tested in order to achieve the best performance and learning of the video dataset provided. In addition it has been studied different kind of post processing over the trained network's output to achieve a better results on the temporally localization of activities on the videos. The results provided by the neural network developed in this thesis have been submitted to the ActivityNet Challenge 2016 of the CVPR, achieving competitive results using a simple and flexible architecture.