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Suspicious Behavior in Outdoor Video Analysis - Challenges & Complexities

Suspicious Behavior in Outdoor Video Analysis - Challenges & Complexities. Air Force Institute of Technology/ROME Air Force Research Lab Unclassified IED test sequences showing dropped package from vehicle (DPV) Combination of motion analysis and change detection

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Suspicious Behavior in Outdoor Video Analysis - Challenges & Complexities

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  1. Suspicious Behavior in Outdoor Video Analysis - Challenges & Complexities • Air Force Institute of Technology/ROME Air Force Research Lab • Unclassified IED test sequences showing dropped package from vehicle (DPV) • Combination of motion analysis and change detection • Homogenous regions and aperture problem and for optic flow approaches • Learning appropriate background for change (ghost objects appear due to slow or fast learning) • Global camera motion/jitter • Occlusion and Camouflage • Environmental problems • Dust and smoke • Wind –local object motion (swaying of branches, shadows) • Precipitation –rain, slow etc. • Clutter (background model) • Illumination problems • Shadows (static and moving cast shadow) - missed objects or false detections • Glare – false detections, object shape and trajectory distortions • Sudden illumination changes (cloud movements) – false detections • Low contrast or color saturation

  2. Dropped package from vehicle (DPV) sequences - Logitech Orbit 20ft Vertical Run 2 Frame 150: Event of interest marked

  3. Detecting Occluded EventSequence - Logitech Orbit 20ft Vertical Run 3 Frame 121: Event of interest marked

  4. Glare and ShadowsSequence - Logitech Orbit 10ft Vertical Run 2 Frame 43: False detection due to glare Frame 141: Event of interest marked

  5. Dust and ShadowsSequence - Logitech Orbit 10ft Vertical Run 3 Frame 159: Event of interest marked Frames 72&170: False detection due to dust

  6. Dust and ShadowsSequence - Logitech QuickCamPro 5000 10ft Vertical Run 3 Frame 91: False detections due to dust Frame 150: Event of interest missed due to shadow and insufficient contrast

  7. Sequence - Logitech QuickCamPro 5000 20ft Vertical Run 2 No event of interest

  8. Sequence - Logitech QuickCamPro 5000 20ft Vertical Run 3 Frame 104: Event of interest marked

  9. Effect of Learning Rate in Background Modeling Sequence - Logitech Orbit 10ft Vertical Run 2 Frame 86: Correct Detection when in motion Frame 210: Ghost object left behind (due to slow background learning using Mixture of Gaussians) when the car starts to move again Frame 134: Object that stops for a while blends into the background

  10. Problems with Flow-based ApproachesSequence - Logitech QuickCamPro 5000 20ft Vertical Run 1 Frame 24: Aperture problem, motion of homogeneous regions is not detected Frame 83: Larger temporal window results in false detections and larger object boundaries Frame 35: Non-moving objects not detected

  11. Suspicious Behavior in Outdoor Video Analysis - Challenges & Complexities • Combination of motion analysis and change detection • Homogenous regions and aperture problem and for optic flow approaches • Learning appropriate background for change (ghost objects appear due to slow or fast learning) • Global camera motion/jitter • Occlusion and Camouflage • Environmental problems • Dust and smoke • Wind –local object motion (swaying of branches, shadows) • Precipitation –rain, slow etc. • Clutter (background model) • Illumination problems • Shadows (static and moving cast shadow) - missed objects or false detections • Glare – false detections, object shape and trajectory distortions • Sudden illumination changes (cloud movements) – false detections • Low contrast or color saturation

  12. Moving Object Detection Approaches Optical Flow Analysis: Characteristics of flow (velocity) vectors of moving objects over time are used to detect changed regions. Advantage: can be used in the presence of camera motion. Disadvantage: usually computationally expensive & aperture problem. Change Detection Background subtraction: Moving regions are detected through difference between the current frame and a reference background image. | framei-Backgroundi |>Th Advantage: provides the most complete feature data. Disadvantage: sensitive to dynamic scene changes due to lighting and extraneous events and cannot handle global motion. Temporal differencing: Similar to background subtraction but the estimated background is the previous frame. | framei-framei-1 |>Th Advantage: very adaptive to dynamic environments. Disadvantage: has problems in extraction of all relevant feature pixels (aperture problem).

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