| Anti-Terrorism has been a global issue and video surveillance has become increasingly popular in public places e.g. banks, airports, public squares, casinos, etc. However, when encountered with the crowd environment, conventional surveillance technologies will have difficulties in understanding human behaviors in crowded environment.;In this thesis, I address the problem of crowd surveillance and present the methodology of how to model and monitor the crowd. The methodology is mainly based on motion features of crowd under human constrains. By utilizing this methodology, dynamic velocity field is extracted and later used for learning. Thereafter, learning technology based on appropriate features will enable the system to classify the crowd motion and behaviors. In this thesis, I tried four topics in crowd modeling and the contributions are in the following areas, namely, (1) robust people counting in crowded environment, (2) the detection and identification of abnormal behaviors in crowded environment, (3) modeling crowd behaviors via human motion constrains, and (4) modeling crowd behaviors using crowd energy.;Firstly, I developed a learning-based algorithm for people counting task in crowded environment. The main difference between this method and traditional ones is that it adopts separated blobs as the input of the people number estimator. The blobs are selected according to their features after background estimation and calibration by tracking. After this, each selected blob in the scene is trained to predict the number of persons in the blob and the people number estimator is formed by combining trained sub-estimators according to a pre-defined rule.;Secondly, I introduced a human abnormal behavior identification system in the crowd based on optical flow features. Optical flow calculation is applied to obtain the velocity field of the raw images and the corresponding optical flows in the foreground are selected and processed. Then, the optical flows are encoded by support vector machine to identify the abnormal behaviors of humans in crowded environments. Experimental results show that this method can handle some places where it is very crowded while the traditional methods can not.;Thirdly, I discussed how crowd modeling using human motion constrains is realized and the quantitative evaluation is given. I declare that the human motion patterns can be added to increase the accuracy and robustness of abnormal behavior identification. In more detail, I applied Bayesian rules to optimize the optical flow calculation result. I also declare that the motion pattern of crowd is similar with that of water when the environment become very crowded and corresponding rules are applied.;In the last part, I discussed the method to analyze the crowd motion from a different angle: by video energies. I mainly use the defined energies to identify the human crowd density and human abnormal behaviors in the crowd. I define two categories of video energies based on intensity variation and motion features and adopt two surveillance methods for the two energies accordingly. Using wavelet analysis of the energy curves, I obtained a result which shows that both methods can be used to deal with crowd modeling and real-time surveillance satisfactorily.;The work in this thesis has provided a theoretical framework for crowd modeling research and also proposed corresponding algorithms to understand crowd behaviors. Moreover, it has potential applications in areas such as security monitoring in public regions, and pedestrian fluxes control, etc. |
