A Probability-based Model for Detecting Abandoned Objects in Video Surveillance Systems
Thi Thi Zin, Pyke Tin, Takashi Toriu, Hiromitsu Hama · 2012
Detection of suspicious packages or abandoned objects is one of the most important tasks in video surveillance systems. Some recent terrorist attacks involving explosive packages left behind in many contexts such as airports, rail stations and etc. illustrate the importance of this problem. In this paper, we propose a probability-based model for robustly and efficiently detecting abandoned objects in complex environments. Specifically, we develop a new probability-based background subtraction algorithm based on combination of multiple background models for motion detection. In addition, several improvements are implemented to the background subtraction method for shadow removal and quick lighting change adaptation. We then analyze the extracted objects to classify as static or dynamic objects. After the analysis, we employ the statistical running average of the static foreground masks for event type decision making either abandoned or very still person. Finally, the robustness and efficiency of the method are tested on our video sequences and PETS2006 datasets.