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Automatic Tracking and Counting of Moving Objects

  • This work presents the conception and development of a software system to detect, track and count automatically moving objects in videos. The aim of the system is the automatically counting of moving turtles on a beach. To achieve a high recognition rate of the moving objects we consider three segmentation strategies (SubstractionGrayscale, SubstractionBinarization, Substraction Canny), two object identification methods (Grayscale Connected, FelzenwalbHuttenlocher) and two object recognition methods Nearest Object Distance, Certain Recognition Matching. In the preprocessing step of our system we select parameters like the interesting area and the object size. We use the segmentation process for the recognition of potential objects. After the identification of objects as turtles we track their movements. In the last step we count the turtles. In order to have a representative test number of moving objects we use videos with 1661 moving cars. The best results of 98.98 percent we reached with the combination Canny Edge Detection,This work presents the conception and development of a software system to detect, track and count automatically moving objects in videos. The aim of the system is the automatically counting of moving turtles on a beach. To achieve a high recognition rate of the moving objects we consider three segmentation strategies (SubstractionGrayscale, SubstractionBinarization, Substraction Canny), two object identification methods (Grayscale Connected, FelzenwalbHuttenlocher) and two object recognition methods Nearest Object Distance, Certain Recognition Matching. In the preprocessing step of our system we select parameters like the interesting area and the object size. We use the segmentation process for the recognition of potential objects. After the identification of objects as turtles we track their movements. In the last step we count the turtles. In order to have a representative test number of moving objects we use videos with 1661 moving cars. The best results of 98.98 percent we reached with the combination Canny Edge Detection, Grayscale Connected, and Certain Region Matching Strategy.show moreshow less

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Metadaten
Author: Jonathan Philipps, Ingrid Bönninger, Martin Weigert, Javier Vasquez
DOI:https://doi.org/10.1109/IWOBI.2014.6913945
ISBN:978-1-4799-6174-0
Title of the source (English):IEEE Conferenc on Bio-inspired Intelligence (IWOBI), 2014, Liberia, Costa Rica, 16-18 July 2014
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of publication:2014
First Page:93
Last Page:97
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Softwareengineering
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