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02170nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8882 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251215153504.0 |
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251215b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.87 M39 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Maza, Mariell Emmanuel July M.; Ortiaga, John Carlo H. |
| 245 ## - TITLE STATEMENT |
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| Title |
An enhancement of CNN applied in multi object tracking system in heterogenous zoological environment |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
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. |
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c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
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text |
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unmediated |
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unmediated |
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unmediated |
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volume |
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volume |
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volume |
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ABSTRACT: The enhancement of the CNN algorithm successfully achieved the three specific objectives set for this study. First, the transition from binary to multi-class species classification significantly improved the model’s capability, with the classification accuracy increasing from 76.4%, and even reaching 100% in controlled evaluation scenarios compared to the original 66.7%. Second, the refinement of the CNN architecture through the integration of EfficientNetB3 resulted in a 33.3% improvement in accuracy, demonstrating superior feature extraction and generalization capabilities over the original basic CNN model, despite a slight increase in inference time. Third, the integration of the enhanced CNN with YOLOv8 for object detection and Deep SORT for object tracking provided real-time detection and tracking capabilities, achieving a mean Average Precision (mAP) of 91.5% and reducing ID switches by 78.26% compared to the baseline MOTHe configuration. These enhancements effectively addressed the limitations of the original algorithm, resulting in substantially higher classification precision, more reliable tracking under complex environmental conditions, and a scalable, modular framework capable of future upgrades. The findings validate that targeted algorithmic improvements in classification, feature extraction, and real-time tracking significantly enhance the |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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Filipiniana |
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academic writing |
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Thesis/Dissertation |