A Policy Based Media Controller in Project ReSCUE Jooyoung Park Mentor: Prof. Sharad Mehrotra Monitoring media-instrumented spaces via vision processing on cameras · · S ummer U ndergraduate 2 R esearch 0 F ellowship in 0 I nformation 6 T echnology Introduction : My project goal is to perform image processing on cameras at the multi-media sensing environment within Rescue’s Responsphere infrastructure. ** Privacy Preserving Workspace: Within such a system, infrastructure that supports privacy-aware constraints can be employed to preserve privacy of monitored individuals. ** Efficient Monitoring System: An automated triggering mechanism can detect high-level events and dispatch necessary alarms as fast as one still image per 1 millisecond. CAMASVM Project and Its Progress 1. The Coffee Cam Project: 2. Rescue Biohazard Drill Video Processing Coffee Cam project aims to detect violations of coffee room policies using images acquired from Linksys WV54G cameras with audio. The coffee room has two cameras, one for capturing mug shots from the coffee machine and the other for capturing a broader area of the coffee room for the purpose of detecting violators. With images received from these cameras, the CAMASVM coffee cam system detects high-level coffee room alerts for events including the brewing of a fresh pot, the emptying of the pot, an accidental leaving of the burner on, and coffee spill. When any of these violations occur, the CAMASVM gets a shot of possible violators. My job in this project was to integrate the work of graduate students to make the system operable. For detecting persons in the images, I used JAI (Java Advanced imaging). Future development: * Development of web-based system that integrates multi-camera infrastructure along with privacy preservation policies. * Face recognition using openCV or similar library will be deployed for person/face recognition of coffee- room violators. * Voice Alarm System: When violation occurs, an automated voice system recorded in.wav file will be played in the coffee room to alert the violator that he/she has made a violation. My job is to process videos from a biohazard drill conducted by the ReSCUE team and detect events including counting the number of people in the camera scope, tracking humans, detecting firefighters and rescue teams by the color of their t-shirts ( bright blue for rescue team and red for firefighters), and performing face recognition. To execute this job, I used openCV (open source computer vision for real time computer vision). Further development: ** Little Sister Recognition: need to detect small mobile robots with attached cameras. ** Event Log: create a log of events with corresponding timestamp. ** real-time processing: Real-time processing using CAMASVM software which produces jpeg files from streaming video may be done using Java. Acknowledgements: Dr. Weigi Yan Cal IT2 Daniel Massaguer, Hojjat Jafapour, Shengyue Ji grad students