Abstract
As the increasing of the bomb attacks in recent years and these attacks are repeatedly concentrated on the public places, such as MRT stations. Establishing a surveillance system with high-tech appliances to against terrorism becomes a critical issue nowadays. In this thesis, an algorithm of finding the abandoned objects in the environment of crowded public places is proposed. There exist some approaches to discover the abandoned objects under the circumstance of two scenarios: 1) The pixels in the scenes of abandoned objects do not intermixed with background pixels; 2) The abandoned objects emerge more often than other moving objects (pedestrians, commuters, etc.) in a surveillance video segment. These approaches work well in the phenomena of occasionally few pedestrians, but may fail in case there are crowded in rush hours; the system would issue many false alarms then. In this study a motion filter is proposed to filter out the partial scenes caused by irrelevant motion objects, together with accumulate the remaining useful pixel information. If we have enough evidence of abandoned objects according to cumulated records, an alarm is issued. We use the Mixture of Gaussian (MoG), which is the most popular background modeling tool, to record the useful history of each pixel. Finally, we use three scenarios to examine the performance of our approach, they are: few, normal and rush hours. The detection accuracy of the system without our motion filter is still satisfying in the environment with casual or normal cases, however it issued numerous false alarms in the environment that is highly crowded. While on the contrary the system with the help of our motion filter will issue the proper alarm for abandoned objects or some few unfavorable alarms for some specific background noises.