| dc.description.abstract | Due to the fast development of computer and video technologies and the cost-down of capturing devices, surveillance systems are gradually widely applied in our daily life. However, the main function of current surveillance systems only focuses on the recording of video event. The developing of automatic and intelligent surveillance systems can detect, track, recognize and analyze moving objects including the behaviors of objects and the occurring of abnormal events, and then issue warring message automatically.
In this thesis, a video surveillance system for abnormal pedestrian behavior analysis is presented. Firstly, background subtraction technique is employed to detect moving objects from video sequences. Then, three key features, including object position, object size, and object color, are extracted to track each detected object. After that, Gaussian Mixture Models (GMM) is introduced to model pedestrians’ behaviors. According to the parameters of the models, different behaviors like walking, running and falling can be successfully recognized and analyzed. Finally, two curve-matching algorithms are employed to complete the trajectory retrieval.
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 |