# A Proposal-Based Solution to Spatio-Temporal Action Detection in Untrimmed Videos

arXiv: https://arxiv.org/abs/1811.08496 · HTML: https://mlsanity.com/paper/1811.08496/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

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

- [MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection](https://mlsanity.com/paper/2112.03902/) (2021)
- [Graph Convolutional Module for Temporal Action Localization in Videos](https://mlsanity.com/paper/2112.00302/) (2021)
- [PAMI-AD: An Activity Detector Exploiting Part-attention and Motion Information in Surveillance Videos](https://mlsanity.com/paper/2203.03796/) (2022)
- [Self-Denoising Neural Networks for Few Shot Learning](https://mlsanity.com/paper/2110.13386/) (2021)
- [Recent Trends in 2D Object Detection and Applications in Video Event Recognition](https://mlsanity.com/paper/2202.03206/) (2022)
- [Activity Classification Using Unsupervised Domain Transfer from Body Worn Sensors](https://mlsanity.com/paper/2304.10643/) (2023)
- [Lightweight Delivery Detection on Doorbell Cameras](https://mlsanity.com/paper/2305.07812/) (2023)

## Abstract (arXiv)

Existing approaches for spatio-temporal action detection in videos are limited by the spatial extent and temporal duration of the actions. In this paper, we present a modular system for spatio-temporal action detection in untrimmed security videos. We propose a two stage approach. The first stage generates dense spatio-temporal proposals using hierarchical clustering and temporal jittering techniques on frame-wise object detections. The second stage is a Temporal Refinement I3D (TRI-3D) network that performs action classification and temporal refinement on the generated proposals. The object detection-based proposal generation step helps in detecting actions occurring in a small spatial region of a video frame, while temporal jittering and refinement helps in detecting actions of variable lengths. Experimental results on the spatio-temporal action detection dataset - DIVA - show the effectiveness of our system. For comparison, the performance of our system is also evaluated on the THUMOS14 temporal action detection dataset.