| dc.description.abstract | In the past decades, most of surveillance systems used infrared rays, radar rays, or microwaves to detect and identify moving targets from monitoring environments, such as airports, station, banks, and restaurants, by manual manner. However, a lot of attentions have been paid to the developing of automatic and intelligent surveillance systems due to the rapid developments of computer and video techniques to monitor your home or your living environments. Intelligent surveillance system can automatically detect, track, and analyze moving objects, including the behaviors of objects and the occurring of unsafe events, and then send warnings message to people without involving any manual efforts.
The main purpose of this thesis is to present a video surveillance system to detect, track, and analyze human behaviors. First of all, we use the technique of background subtraction to detect different moving objects from video sequences. Then, two key features, i.e., object sizes and colors are utilized to track each detected object and its actions. After that, we introduce the theory of Gaussian Mixture Models (GMM) to model the behaviors of objects. According to the parameters of the models, different object behaviors like running, walking, and falling can be successfully recognized and analyzed. Experimental results show that the proposed method offers great improvements in terms of accuracy, robustness, and stability in the analysis of object behaviors. | en_US |