Abstract—In video surveillance using overhead cameras, it is very important to capture and regularly updated the background to enable accurate extraction of persons or objects of interest. However, in scenes with a lot of movement and stationary objects, for example a train station, it is not easy to update the background without including such objects. In this work, we investigate several objects features and combine them to maintain a stable background image. The features include the speed of the object, texture, shape, associations between persons and objects, etc. The data used is a subset of the i-Lids dataset that was captured for analyzing video systems. It is captured in a train station using one overhead camera. Each video segment is about three minutes long.
Index Terms—Active background, abandoned objects, shape matching.
The authors are with the graduate School of Advanced Technology and Science, The University of Tokushima, Janpan (e-mail: firstname.lastname@example.org).
Cite: Stephen Karungaru, Kenji Terada, and Minoru Fukumi, "Automatic Background Updating for Abandoned Object Detection at Train Stations," International Journal of Machine Learning and Computing vol. 2, no. 5, pp. 609-613, 2012.