UnCanny : Exploiting Reversed Edge Detection as a Basis for Object Tracking in Video
Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang-Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal "false positive" noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:7 |
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| Enthalten in: |
Journal of imaging - 7(2021), 5 vom: 23. Apr. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Honeycutt, Wesley T [VerfasserIn] |
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| Links: |
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| Themen: |
Edge detection |
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| Anmerkungen: |
Date Revised 03.04.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/jimaging7050077 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang-Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal "false positive" noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods | ||
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