Base Protection Lab (BPL) December 12, 2007 ONR Program Officer: William “Kip” Krebs, , Alternate POC: Annetta Burger , Presentation to BMVA Symposium on Security and surveillance: performance evaluation

Page 2 Objectives Conduct basic research to provide more open public access to recreational and other non-restricted facilities on military bases and to improve the overall base safety and security utilizing advanced video and signal based surveillance. Create test bed in Hawaii to evaluate whether novel sensors (video cameras, radio frequency identification, seismic, LIDAR, microwave, and infrared sensors) in combination with behavior analysis software can identify patterns of behavior.

Page 3 PMRF BPL System Diagram Alert Delivery & Situation Data Data Collection (Integrated Sensor Systems) RFIDSeismic Sensor Data Links (Wireless) Production System – Data Analysis and Management External (Wide Area) Network Firewall Remote Users and Developers: – SAIC Kauai – SAIC Maui – SAIC Arlington – UH Manoa – Novasol Oahu – Object Video – MHPCC Maui – Others Internet PMRF Base Protection Laboratory (PBPL) MW / IRLidarTextualVideoOthers Middleware Data Storage & Archiving Simulation & Playback Data Fusion & Tracking Behavioral Analysis - Automated Behavior Analysis (ABA) - Statistical Anomaly Detection (SAD) - Agent Based Modeling (ABM) Sensor Data Processing Alert Generation Base Security Development Platform

Page 4 Current BPL Sensor Configuration 22°00 ’ ” N 159°46 ’ ” W Outdoor Test Bed –4.5km by 1km area –Linked by wireless communications Open Architecture Hardware Configuration –Mobile sensors enable optimum sensor coverage for data collection exercises Software Configuration – modular, flexible and reusable –Object-oriented software –Based on Java Jini architecture –Communication through a “ blackboard ”

Page 5 Sensors RFID System – UltraWideBand 2 LIDAR motion trackers 5 License Plate Readers 8 Low Light Cameras 4 Video Cameras Seismic Array (2000 feet) 2 MW/IR Fence Posts (500 feet fence)

Page 6 Data Collection –Scripted Scenarios for Abnormal Activity 4 Days data of confederates –Normal Data 2 Days data of base personnel and recreational visitors Sensor Collection Results –12 video cameras with advanced video analytics software detected and tracked objects of interest without problems –LIDAR – converted for intrusion detection and alarm sensor Detected and provided accurate alerts –Microwave and Infrared Detection Fences 100% detection and 0% false alarms for motion detection –RFID Limited coverage to be improved with tuning antennas and replacing fixed sensors with mobile units –Seismic No data available due to compatibility issues Sensors should be available Jan08

Page 7 Proposed RFID Coverage 2D coverage (coordinates) Seen by three or more receivers Provides (x, y) with 3m or better accuracy 1D coverage (a point between two sensors) Seen by two receivers Provides a point (x Σ, y Σ ) on a line between the two sensors. 0D coverage (presence in an area) Seen by one receiver Provides the coordinates of the sensor (x S, y S ) that sees the entity No detection (no information) Seen by none of the receivers

Page 8 RFID Track

Page 9 Video Tracks

Page 10 LIDAR Sensor 1000 feet

Page 11 LIDAR Tracks System Administrator Display - pedestrian and vehicle traffic map overlay

Page 12 Challenges Develop a reliable, plug-play, robust test bed to meet researcher requirements. Develop common standard data sets that vary in difficulty that can be used to assess algorithm performance. Develop valid and reliable metrics to evaluate and compare behavioral algorithm performance.

Page 13 Future Plans Create common data sets and metrics. Distribute data sets to research community to test whether behavioral algorithms can detect “ normal ” and “ anomalous ” behavior for the prediction of threats and advance warning using a variety of sensors. Test and evaluate sensor options to optimize early detection of threat behavior and reduce security manpower requirements.