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Automated Vision Systems for Tracking and Mapping Urban Wildlife Activity

  • Unique Paper ID: 168421
  • Volume: 11
  • Issue: 5
  • PageNo: 1041-1044
  • Abstract:
  • As urban areas continue to expand, human-wildlife interactions become increasingly frequent, raising concerns about biodiversity conservation, ecosystem health, and wildlife management. This paper presents an AI- powered Urban Wildlife Monitoring System that utilizes computer vision and artificial intelligence to identify and map wildlife activity patterns in urban environments. The proposed system performs real-time detection and classification of various wildlife species using a YOLOv8-based object detection model. It logs the location and timestamps of wildlife sightings, generating detailed maps and heatmaps to track movement patterns, hotspots, and migration trends. The system aims to assist city planners and conservationists by providing insights into urban biodiversity, facilitating wildlife-friendly urban planning, and reducing human-wildlife conflicts. Advanced features such as behavior analysis, activity pattern recognition, and automated alerts for authorities further enhance the system's capabilities. The system maintains data integrity and privacy by anonymizing sensitive information and storing it securely. With its scalable architecture and real-time data insights, this AI-powered system offers a novel approach to urban wildlife monitoring, aiding conservation efforts and improving coexistence in increasingly urbanized landscapes.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 1041-1044

Automated Vision Systems for Tracking and Mapping Urban Wildlife Activity

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