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Summary For The Test Sequences

Summary For The Test Sequences. Logitech Orbit 10ft Vertical Run 2. Frame 43: False detection due to glare. Frame 141: Event of interest. Logitech Orbit 10ft Vertical Run 3. Frame 159: Event of interest. Frames 72&170: False detection due to dust. Logitech Orbit 20ft Vertical Run 2.

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Summary For The Test Sequences

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  1. Summary For The Test Sequences

  2. Logitech Orbit 10ft Vertical Run 2 Frame 43: False detection due to glare Frame 141: Event of interest

  3. Logitech Orbit 10ft Vertical Run 3 Frame 159: Event of interest Frames 72&170: False detection due to dust

  4. Logitech Orbit 20ft Vertical Run 2 Frame 150: Event of interest

  5. Logitech Orbit 20ft Vertical Run 3 Frame 121: Event of interest

  6. 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. Logitech QuickCamPro 5000 20ft Vertical Run 2 No event of interest

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

  9. Challenges & Complexities • Motion versus change detection • Aperture problem 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 • Precipitation –rain, slow etc. • Wind –local object motion (swaying of branches, shadows) • Clutter (background model) • Dust and smoke • Illumination problems • Shadows (static and moving cast shadow) - missed objects or false detections • Sudden illumination changes (cloud movements) – false detections • Glare – false detections, object shape and trajectory distortions • Low contrast or color saturation

  10. 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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