Taking as reference the concept of agent in the artificial intelligence, this paper proposes a new multi-agent approach which can be employed in the surveillance for a people group in public places rather than a single person. The agent embodies the state and logic relationship between the person which the agent represents and the others in the same group. It does not merely stand for such individual information of persons as given by the existing surveillance systems. The results of experiments show that by using our multi-agent approach to compute and analyze the relationship between a number of agents, we can well perform real-time surveillance for the group and the related events so as to enhance the applicability and intelligence level of surveillance systems

In this paper, we developed a sensor-integrated shoe as an information acquisition platform to sense the foot motion. The system is small, portable and wearable. The platform is mainly composed of four parts including a sensing module, a computing module, a wireless communication module, and a data visualization module. Based on this platform, we developed a novel input device called Shoe-Mouse, which can be used by people who have difficulties in using their hands to operate computers or devices. We evaluated the performance of Shoe-Mouse, and the initial experimental results demonstrated the function. The platform can be also used for applications such as gait recognition, human identification, and motion monitoring.

Surveillance of public places has become a world-wide concern in recent years. The ability to classify human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of different human walking trajectory patterns is an important step towards the achievement of this goal. In this research, we utilize the approach of Longest Common Subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks. The LCSS similarity approach is also compared with a similarity measure based on hidden Markov model. We found that the boundary establishing method based on learning by support vector regression gives the best results using real-life data during testing.

Human action understanding is crucial to the success of many human-machine interfaces based on vision. In this research, we apply artificial intelligence and statistical techniques towards observation of people, leading to modeling of their actions, and understanding of their intentions. In order to actualize the paradigm of learning from demonstration, a tracking system that is capable of locating the head and hand positions of moving humans has been developed. We propose to classify the motion trajectories of humans in the scene by using support vector classification. Since the data size of human motion trajectories is large, we apply principal component analysis (PCA) and independent component analysis (ICA) for data reduction. We have successfully applied the developed technique on two different applications: action recognition of table tennis players, and detection of human fighting motions.

We are developing a novel robot concept called the wearable robot. Wearable robots are mobile information devices capable of supporting remote communication and intelligent interaction between networked entities. In this paper, we explore the possible functions of such a robotic network and will present a distributed network architecture based on service components. In order to support the interaction and communication between the components in the wearable robot system, we have developed an intelligent network architecture. This service-based architecture involves three major mechanisms. The first mechanism involves the use of a task coordinator service such that the execution of the services can be managed using a priority queue. The second mechanism enables the system to automatically push the required service proxy to the client intelligently based on certain system-related conditions. In the third mechanism, we allow the system to automatically deliver services based on contextual information. Using a fuzzy-logic-based decision making system, the matching service can determine whether the service should be automatically delivered utilizing the information provided by the service, client, lookup service, and context sensors. An application scenario has been implemented to demonstrate the feasibility of this distributed service-based robot architecture. The architecture is implemented as extensions to the Jini network model.

Changing facial expressions is a natural and powerful way of conveying personal intention, expressing emotion and regulating interpersonal communication. Automatic estimation of human facial expression intensity is an important step in enhancing the capability of human-robot interfaces. In this research, we have developed a system which can automatically estimate the intensity of facial expression in real-time. Based on isometric feature mapping, the intensity of expression is extracted from training facial transition sequences. Then, intelligent models including cascade neural networks and support vector machines are applied to model the relationship between the trajectories of facial feature points and expression intensity level. We have implemented a vision system which can estimate the expression intensity of happiness, anger and sadness in real-time.

We are developing a network architecture for our novel robot concept of wearable robot. Wearable robots are mobile information devices capable of supporting remote communication and intelligent interaction between networked entities. In this paper, a service-based wearable robot network architecture that involves extensions to the Jini network model is presented. We discuss three extensions to the original Jini model. The first extension involves the incorporation of a task coordinator service such that the execution of the services can be managed using a priority queue. The second extension enables the system to automatically push the required service proxy to the client intelligently based on certain system-related conditions. In the third extension, we allow the system to automatically deliver the services based on contextual information. Using a fuzzy-logic-based decision making system, the matching service can determine whether the service should be automatically delivered utilizing the information provided by the service, client, lookup service and context sensors. An application scenario has been implemented to demonstrate the feasibility of this distributed service-based robot architecture.

Surveillance of public places has become a world-wide concern. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we have developed an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. It takes into account both the local and global characteristics of the observed trajectories and be able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing.

We model human sensations in virtual reality applications using cascade neural networks. In the modeling process, the dimension of inputs presented to the humans and the sensation systems may be very high. In this research we propose using the independent component analysis (ICA) to achieve input reduction. We obtain human sensation data from a full-body motion virtual reality interface - "motion-based movie". A fixed-point ICA algorithm is applied to achieve feature extraction and input selection for reducing the dimension of the environmental stimulus data. The fidelity of the sensation models trained using the reduced inputs is verified by the hidden Markov model based similarity measure. The performance of input reduction using ICA is compared with that using the principal component analysis. Experimental results showed that the input selection scheme based on ICA is capable of improving the modeling performance of the computational sensation systems and reducing the input dimension by 60%.

This paper aims to study human-machine integration in the human sensation aspect. We propose using cascade neural networks to model human sensation during the interaction, between humans and machines. The fidelity of the sensation models is verified using a hidden Markov model (HMM)-based similarity measure scheme. We applied this modeling technique in a full-body motion virtual reality interface-"motion-based movie". The sensation levels of the human participants in this application were modeled effectively by the cascade neural networks and the fidelity of the models were revealed by the HMM similarity measure scheme.