Online Learning Classification for Video Monitor
- Zhiyuan Li, Chao Wang, Xiaoduo Zhang
- Corresponding Author
- Zhiyuan Li
Available Online June 2017.
- https://doi.org/10.2991/ammee-17.2017.62How to use a DOI?
- Online learning, Unsupervised, Objects classification, Video Monitor.
- This paper presents an online unsupervised learning classification of pedestrians and vehicles for video Monitor. Different from traditional methods depending on offline training, our method adopts the online label strategy based on temporal and morphological features, which saves time and labor to a large extent. It extract the moving objects with their features from the original video. An online filtering procedure is adopted to label the moving objects according to certain threshold of speed and area feature. The labeled objects are sent into a SVM classifier to generate the pedestrian & vehicle classifier. Experimental results illustrate that our unsupervised learning algorithm is adapted to polymorphism of the pedestrians and diversity of the vehicles with high classification accuracy.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zhiyuan Li AU - Chao Wang AU - Xiaoduo Zhang PY - 2017/06 DA - 2017/06 TI - Online Learning Classification for Video Monitor BT - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/ammee-17.2017.62 DO - https://doi.org/10.2991/ammee-17.2017.62 ID - Li2017/06 ER -