Abstract
Object and human tracking in streaming videos are one of the most challenging problems in vision computing. In this article, we review some relevant machine learning algorithms and techniques for human identification and tracking in videos. We provide details on metrics and methods used in the computer vision literature for monitoring and propose a state-space representation of the object tracking problem. A proof of concept implementation of the state-space based object tracking using particle filters is presented as well. The proposed approach enables tracking objects/humans in a video, including foreground/background separation for object movement detection.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | International Journal of Information Systems in the Service Sector |
| Volume | 11 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2019 |
Keywords
- Background Exclusion
- OpenCV
- Visual Object Tracking
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Strategy and Management
- Management Science and Operations Research
- Information Systems and Management
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Llano, C. R., Ren, Y., & Shaikh, N. I. (2019). Object detection and tracking in real time videos. International Journal of Information Systems in the Service Sector, 11(2), 1-17. https://doi.org/10.4018/IJISSS.2019040101