Large-Scale Mapping of Human Activity Using Geo-Tagged Videos
| Title | Large-Scale Mapping of Human Activity Using Geo-Tagged Videos |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Zhu, Yi, Liu, Sen, Newsam, Shawn |
| Conference Name | Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5490-5 |
| Keywords | activity recognition, convolutional neural networks, Deep Learning, deep video, geographic visualization, Metrics, pubcrawl, resilience, Resiliency, Scalability, spatio-temporal analysis |
| 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 map activities both spatially and temporally. |
| URL | https://dl.acm.org/doi/10.1145/3123266.3123381 |
| DOI | 10.1145/3139958.3140055 |
| Citation Key | zhu_large-scale_2017 |