2 nd Workshop on Smart Surveillance System Applications

Motivation and Justification: Automatic recognition of people and their activities has very important social implications, because it is related to the extremely sensitive topic of civil liberties. Society needs to address this issue of automatic recognition and find a balanced solution that is able to meet its various needs and concerns. In the post 9/11 period, population security and safety considerations have given rise to research needs for identification of threatening human activities and emotional behaviours. Timely identification of human intent is one of the most challenging areas of all-hazards risk assessment in the protection of critical infrastructure, business continuity planning and community safety. The all-hazards approach is used extensively by the public and private sector, including Public Safety Canada (PS Canada -- formerly PSEPC), Emergency Management Ontario (EMO), US Federal Emergency Management Agency (FEMA) and US Department of Homeland Security (DHS). There is a clear need for industry and the research community to addresses fundamental issues involved in the prevention of human-made disasters, namely the variable context-dependent, real-time detection/identification of potential threatening behaviour of humans, acting individually or in crowded environments. Such an industry and academia forum will have to discuss development and commercialization of new multimodal (video and infrared, voice and sound, RFID and perimeter intrusion) intelligent sensor technologies for location and socio-cultural context-aware security risk assessment and decision support in human-crowd surveillance applications in environments such as school campuses, hospitals, shopping centers, subways or railway stations, airports, sports and artistic arenas etc. Due to the complexity of the surveillance task there is a clear need for the development of a distributed intelligent surveillance system architecture, which combines visual and audio surveillance based on wireless sensor nodes equipped with video or infrared (IR) cameras, audio detectors, or other object detection and motion sensors with location aware wireless sensor network solutions. The integration of visual, sound and radio tracking methods results in a highly intelligent, proactive, and adaptive surveillance and security solution sensor networks. Task-directed sensor data collection and observation planning algorithms need to be developed to allow for a more elastic and efficient use of the inherently limited sensing and processing capabilities. Each task a sensor has to carry out determines the nature and the level of the information that is actually needed. There is a need for selective environment perception methods that focus on object parameters that are important for the specific decision to be made for the task at hand and avoid wasting effort to process irrelevant data. Multisensor data fusion techniques should be investigated for the dynamic integration of the multi-thread flow of information provided by the heterogeneous network of surveillance sensors into a coherent multimodal model of the monitored human crowd. In the context of crowds, robust tracking of people represents an important challenge. The numerous sources of occlusions and the large diversity of interactions that might occur make difficult the long-term tracking of a particular individual over an extended period of time and using a network of sensors. Realtime image processing and computer-vision algorithms need to be studied for the identification, tracking and recognition of gait and other relevant body-language patterns of the human agents who can be deemed of interest for security reasons. Real-time signal processing algorithms have to be designed for the identification and evaluation of environmental and human-subject multimodal parameters (such as human gait, gestures, facial emotions, human voice, background sound, ambient light, etc.) that provide the contextual information for the specific surveillance activity. A multidisciplinary, context-aware, situation-assessment system, including human behaviour, cognitive psychology, multicultural sociology, learning systems, artificial intelligence, distributed multimedia and software design elements, has to be ultimately developed for the real-time evaluation of the activity and emotional behaviour of the human subjects identified as being potentially of security interest in the monitored dynamic environment. The development of such a complex system requires the seamless integration of new and improved surveillance techniques and methodologies supporting both functional and non functional requirements for surveillance networks. Functional requirements are signal processing functions and data fusion, archiving and tracking human behaviours, assessment and interpretation functions of the data, and supporting human decision makers, among others. Non-functional requirements include interoperability, scalability, availability, and manageability. The partial and heterogeneous sensor-views of the environment have to fuse into a coherent Virtualized Reality Environment (VRE) model of the explored environment. Being based on information about real/physical world objects and phenomena, as captured by a variety of sensors, VREs have more real content than the pure Virtual Reality environments entirely based on computer simulations. The VREs model of the explored environment allows human operators to combine their intrinsic reactive-behavior with higher-order world model representations of the immersive VRE systems. A synthetic environment will eventually be needed to provide efficient multi granularity-level function-specific feedback and human-computer interaction interfaces for the human users who are the final assessors and decision makers in the specific security monitoring situation. An ideal system should provide efficient multi granularity-level function-specific feedback for the human users who are the final assessors and decision makers in the specific security monitoring situation. The rate at which surveillance systems can currently disseminate data to evaluate new threats is mainly limited due to the developed and implemented nature of existing systems and their limited ability to operate with other systems. IBM's Service-Oriented Architecture (SOA) provides the much needed deployment ready solution which supports the integration of external systems developed by diverse industrial and institutional partners.

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