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Updated: Aug 2, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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Vehicle Instance Segmentation Polygonal Dataset for a Private Surveillance System.

Najmath Ottakath1, Somaya Al-Maadeed1

  • 1Department of Computer Science and Engineering, Qatar University, Doha P.O. Box 2713, Qatar.

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Summary

This study introduces a privacy-preserving vehicle identification system using instance segmentation and vehicle make classification. The method achieves high accuracy in both segmentation and classification for traffic surveillance.

Keywords:
classificationinstance segmentationmosaic-tiled augmentationvehicle make classification

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Traffic Surveillance

Background:

  • Vehicle identification is crucial for traffic monitoring but raises privacy concerns.
  • Automated surveillance requires tasks like vehicle segmentation, classification, and license plate detection.
  • Instance segmentation can create unique vehicle representations while preserving privacy.

Purpose of the Study:

  • To develop a privacy-preserving automated vehicle identification system.
  • To apply instance segmentation for vehicle frontal region extraction.
  • To identify vehicle make and license plates from segmented regions.

Main Methods:

  • Utilized Mask R-CNN for instance segmentation and vehicle make classification.
  • Annotated a dataset with polygonal bounding boxes for frontal regions and license plates.
  • Employed data augmentation techniques, including mosaic-tiling, for dataset generalization.

Main Results:

  • Achieved 99.2% classification accuracy for vehicle make.
  • Obtained a 99.67% mean Average Precision (mAP) for segmentation.
  • Mosaic-tiling data augmentation improved dataset accuracy and generalization.

Conclusions:

  • The proposed method enables privacy-preserving vehicle identification and re-identification.
  • Instance segmentation effectively extracts relevant vehicle features while maintaining privacy.
  • Data augmentation significantly enhances model performance and generalization capabilities.