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Motion Detection Challenges and Approaches

This summary discusses the challenges and complexities of motion detection, including glare, dust, occlusion, and illumination problems. It also explores different approaches such as optical flow analysis and background subtraction.

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Motion Detection Challenges and Approaches

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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. Logitech Orbit 10ft Vertical Run 2 Incorrect Learning Rate in Background Subtraction Frame 86: Correct Detection when in motion Frame 210: Ghost object left behind when the car starts to move again Frame 134: Object that stops for a while blends into the background

  10. Logitech QuickCamPro 5000 20ft Vertical Run 1Problems in Flow Based Approaches 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 are not detected

  11. 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

  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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