# Large-Scale Mapping of Human Activity using Geo-Tagged Videos

## TL;DR

This paper introduces a novel method for large-scale spatio-temporal mapping of human activities using geo-tagged videos and deep learning, demonstrating real-time analysis and improved accuracy over traditional tag-based methods.

## Contribution

It is the first to utilize deep learning for mapping human activities in videos for smart-city applications, emphasizing real-time processing and visual content analysis.

## Key findings

- Accurately maps activities spatially and temporally
- Real-time processing capability demonstrated
- Visual content outperforms tags/titles in activity recognition

## Abstract

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.