Camera motion estimation and object extraction using Multiple Consecutive Frames with ghost and noise removal for Pan-Tilt-Zoom Camera
This research presents a new algorithm for extracting moving objects using Pan-Tilt- Zoom (PTZ) camera. Previously, system that uses moving camera faces some problems, including misalignment between current and template frames, appearance of unwanted objects (ghost), illumination changes, shadow...
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| Format: | Thesis |
| Language: | English |
| Subjects: | |
| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44129/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44129/2/full%20text.pdf |
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| Summary: | This research presents a new algorithm for extracting moving objects using Pan-Tilt-
Zoom (PTZ) camera. Previously, system that uses moving camera faces some problems,
including misalignment between current and template frames, appearance of unwanted
objects (ghost), illumination changes, shadow and crowd. This research developed an
algorithm to avoid misalignment images, remove ghost and noises. The proposed
algorithm consists of six steps, which are camera motion estimation, object extraction,
removing ghost, detecting shadow, refining shadow and noises elimination. This
research proposed to apply camera motion estimation twice, which is between three
consecutive frames. Keypoints of each image are detected using Speed-up Robust
Features (SURF) detector, then produces homography matrix. The homography
contains rotation and translation of one image from another image. It is used to warp
previous frames with respect to the current frame. In object extraction, current frame is
compared to both compensated previous frame 1 and compensated previous frame 2
using Wronskian Change Detector (WCD). Detecting changes using multiple frames
produces ghost, which is actually moving objects in previous frames. Then, this
research has developed a ghost removal technique, in which two output images of
object extraction are compared each other, pixel by pixel. Then the existing method of
shadow removal, Normalized Cross-Correlation (NCC) technique is applied to refine
the output image. Some pixels may be misclassified as shadow pixels. Therefore,
refinement of shadow detection is done so that the actual shadow is removed, while the
false detected shadows are returned to be background entities. To remove other noises, a
3x3 noise filter has been created. The filter is used to scan the output image where the
centre of the 3x3 window will look for white pixel. Number of white pixel in the whole
window (filter) will be compared to the threshold. Finally, morphological operator is
used to remove undesirable foreground pixels. The developed algorithm had been tested
on seven image conditions; striped background, objects move slowly, camouflage
object, small moving object, multiple moving objects, objects move towards the camera
and shadow. The developed algorithm has successfully extracted moving object,
removed ghost, removed shadow, noises and detected illumination changes. Based on
the manual calculation and visual observations, this system has the highest average
accuracy which is 95.13%, followed by single WCD 93.99%, Background Subtraction
93.42%, Luminance Ratio 92.92%, HSV Histogram 91.85% and Greyscale Histogram
91.47%. |
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