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IBM SVS Analytic Use Case Analysis

IBM SVS Analytic Use Case Analysis. Rick Kjeldsen. Overview. This document shows fuel station cameras with proposed analytics that address requested use case scenarios. Additional alerts are possible on many cameras including: Night Loitering where crime or vandalism is a possibility

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IBM SVS Analytic Use Case Analysis

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  1. IBM SVS Analytic Use Case Analysis Rick Kjeldsen

  2. Overview • This document shows fuel station cameras with proposed analytics that address requested use case scenarios. • Additional alerts are possible on many cameras including: • Night Loitering • where crime or vandalism is a possibility • Abandoned object • Where bombings or other terrorism are a possibility • Illegal Parking

  3. Rovinj cam 16 • Use case: Loitering • Alert on people who remain in store for long periods of time • Discussion • Loitering time has not been specified. • The longer the loitering time, the less reliable loitering detection becomes. Maximum practical loitering time is 1.5 to 4 minutes, depending on conditions. • Maximum loitering for a camera must be determined empirically. The provided video has no examples of loitering activity longer than 2 minutes. • Next actions • Work with customer to determine desired loitering time. • Provide video with several realistic examples of loitering longer than that time.

  4. Rovinj cam 16 • Use Case: Activity analysis • Activity over time • Histogram • Activity levels in different parts of store • Heatmaps or customer counts • Global or individual customer behavior patterns • Track Summary • Discussion • Any area of the store is suitable • Results will be approximate. Counting errors of at least 15% should be expected.

  5. Rovinj - cam 15 • Use cases • Drive-off detection • Provide alerts to the POS when cars arrive at or depart • Wrong direction • Detect if a vehicle enters the pump stations from the right • Car color • Record the color of any car in the region • Discussion • For Drive-off detection, POS must have ability to recover from both missing and false positive alerts.

  6. Rovinj - cam 15 • Use cases • Abandoned Object • Will have some false alerts due to cars at the pumps and objects moved in front of the building • Illegal parking • If applicable

  7. BS Litijska - cam 02 • Use cases • Drive-off detection • Provide alerts to the POS when cars arrive at or depart • Car color • Record the color of any car in the region

  8. BS Litijska - cam 03 • Use cases • Abandoned Object • Will have some false alerts due to shadows, parked cars and other transient activity • Illegal parking • If applicable

  9. BS Litijska - cam 04 • Use cases • Drive-off detection • Provide alerts to the POS when cars arrive at or depart • Car color • Record the color of any car in the region • Discussion • Information from top of image is unusable. Ignore to help avoid errors. Ignore

  10. BS Litijska - cam 05 • Use cases • Drive-off detection • Provide alerts to the POS when cars arrive at or depart • Car color • Record the color of any car in the region • Discussion • Cars partially out of alert RoI are not a problem.

  11. BS Litijska - cam 07 • Use cases • Illegal parking • Car color • Record the color of any car in the region • Loitering • Pre-empt vandalism

  12. BS Litijska - cam 10 & 11 • Use case: Loitering • Alert on people who remain in store for long periods of time • Discussion • Results are not likely to be good on cam 10: • Field of view convers only part of store • People often disappear behind racks • Much of usable area is line for counter, where loitering is expected. • Cam 11 will do somewhat better • Loitering is not detected when person moves between cameras

  13. BS Litijska - cam 10 & 11 • Use case • Person Search: Shirt color • Discussion • Forensic search for people based on their primary color (usually shirt color) • Currently one color per person • Next release (4.0) will have ability to search using multiple simultaneous color on both shirt and pants.

  14. BS Litijska - cam 10 & 11 • Use Case: Activity analysis • Activity over time • Histogram • Activity levels in different parts of store • Heatmaps or customer counts • Global or individual customer behavior patterns • Track Summary • Discussion • People disappear behind racks, especially in cam 10, making results less accurate. • Significant count and tracking errors are possible at any point in time, but longer term trends should be accurate.

  15. BS Litijska - cam 14 & 15 • Use Cases • Number of people behind counter • Excessive activity behind counter • Forensic search & alert on: • Shirt color of customers in line • Shirt color of clerk • Estimate line length (cam 15 only) • Global trends should be accurate, but significant momentary errors possible. • Activity patterns of clerk • Tracks • Heatmap • Time in front of register • Requires additional development

  16. Technical notes • Outdoor color search will work daytime only unless good lighting is available. • Abandoned bag alerts that overlap with parking zones may trigger an unacceptably high number of false alerts. • The accuracy of behavioral analytics, such as loitering, depends on the normal patterns of activity in the scene

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