Public safety/security (PSS) is the most basic human need and on a college campus it is a must for college students to focus on their academics as they prepare to be productive citizens and faculty/staff to safely practice their educational mission. Improving PSS will also drive economic development in the surrounding communities. This project aims to identify social and cultural challenges of PSS on Marquette University (MU) campus with its surrounding communities and conduct a preliminary feasibility study of partly automating the current security monitoring infrastructure to improve detection of incidents while addressing the community concerns. The results of this project will be used to write an SCC-IRG grant proposal with the long-term objective and the national impact of creating a blueprint that is adaptable to thousands of college campuses in the US to establish an automated network of smart sensors for maintaining safe and secure zones on these campuses and their surrounding neighborhoods. This will improve the existing monitoring by law enforcement officers or security staff, and it will provide more reliable and actionable information for incident management.

The main objectives of this proposal are to: a) Understand in-depth the PSS challenges and the community concerns regarding the use of certain technologies on MU campus and in adjacent communities, b) Determine if existing technologies in multi-modal sensing and AI are sufficient to enable the level of automation that will lead to substantially enhanced diagnosis and prognosis decisions made by human operators, c) Combine multi-modal sensory data for cross-correlation in order to improve detection and predictive capabilities, d) Decide which features and system components need to be improved to enable real-time operation, e) Characterize the relation between the observables such as behavioral patterns, and PSS incidents. The scope of the proposed work and the methods and approaches used for each research item will be to: (i) Explore the PSS issues and challenges on MU campus and its periphery in collaboration with the MU Police Department and social scientist colleagues with expertise in crime mapping, analysis, and prevention, (ii) Solicit feedback from relevant stakeholders through community meetings and multidisciplinary workshops, and (iii) Conduct initial feasibility studies involving multi-modal (e.g. camera and audio) data together with contextual information simulating various scenarios (e.g. robbery in its early stages and tracking a suspect) in our labs to see whether some behavioral patterns can automatically predict certain PSS incidents with the help of MU Police Department. The intellectual merit of this proposal is in its paving the way to novel community-supported and data-driven solutions involving advances in real-time image processing, machine learning, predictive analysis, statistical detection and estimation toward developing a smart sensor network to enhance PSS.