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AI-based visitor counting from photo and video data
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Visitors recognition and counting with YOLO
Visitors recognition and counting with YOLO
Image source: Valletta © 2025 All rights reserved

Use of AI-based image and video analysis software to automatically detect and count visitors from camera data, supporting continuous and non-intrusive monitoring of visitor numbers and flows in protected areas.

Key characteristics

Work step
Data analysis
Tool purpose
Photo/Video Data
Properties
Experimental
Keywords
Classification
Identification
HUMANITA
Visitor monitoring
AI
Camera

Tool description

AI-based software for photo and video analysis uses computer vision and machine learning algorithms to automatically detect, classify, and count visitors captured by fixed or mobile cameras installed along trails or access points. In addition to pedestrian counting, the technique can identify and distinguish different types of vehicles (e.g. bicycles, cars, other motorized or non-motorized vehicles), supporting a more detailed characterization of visitor flows and modes of access in protected areas.

Constraints

  • Requires advanced technical expertise for system setup, model configuration and training.
  • Performance depends on image quality, lighting conditions, and camera positioning.
  • Large amount of collected data, requiring adequate memory storage and/or data retrieval options.
  • Potential biases related to algorithm training data (e.g. vehicles recognition).
  • Privacy and data protection constraints may limit deployment and data storage.

Requirements

  • Fixed or mobile cameras with suitable resolution and field of view
  • AI software trained or configurable for outdoor and natural environments
  • Computing infrastructure for data processing (local or cloud-based)

Tool Impact

AI-based photo and video analysis may raise privacy concerns related to the recording of visitors, even when data are anonymized or processed automatically. The installation and maintenance of cameras can cause localized disturbance to vegetation and fauna, particularly if repeated site visits are required.

Best Practices

  • It is important to address both the classification and counting stages: the tested configuration within Interreg CE HUMANITA uses a YOLOv11-based object detection model for the identification and classification of visitors and vehicles, combined with a custom algorithm implementing a virtual “gate” positioned at the center of the camera field of view to count objects crossing the predefined line. Different models could be implemented according to specific needs, as well as the selection of the most appropriate format (photo, video) for the analysis.

Helpful hints to use the tool proficiently

  • Carefully plan camera placement to ensure adequate coverage while minimizing the capture of identifiable personal features. Related to this, consider the implementation of additional algorithms to anonymize the subjects (e.g., pixelation).
  • Verify in advance which elements the AI model is able to recognize and classify (e.g. pedestrians, bicycles, cars, other vehicles) and assess their relevance for the monitoring objectives.
  • Pay particular attention to data retrieval and management, considering automated data transfer solutions due to the large volume of photo and video data collected. For the same reason, especially in case of camera traps usage, verify the possibility of adapting the quality and duration of the acquisition process to optimize the tool efficiency.

Specification

Category Software
Platform
Desktop
Operating system
OS-independent
ModeOnly offline

Linked tools

Category Tool title and description
Study object
Study focus
Work step
Tool purpose
 Classic Professional Free to use Experimental
Automatic visitor counters

Automated sensor-based systems for quantifying visitor numbers and analyzing temporal and spatial visitation patterns in natural areas.
Camera traps for visitor monitoring

Passive recording of human presence along trails and access points using motion-triggered camera traps.
GPS Loggers for visitor monitoring

Portable devices used to record visitor movement patterns, routes, speed, and stay times, providing spatially explicit data to better understand visitor flows and site use.
MegaDetector Video Counting

Adapted MegaDetector algorithm (AI model that identifies animals, people and vehicles in camara trap images) to count animals, people and vehicles in short videos.
Mobile phone network data for visitor monitoring

Use of anonymized and aggregated mobile network event data to estimate visitor numbers and origin areas for selected protected areas and surrounding regions.

Gallery

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Legend

Study objects

Microbiota
Humans
Animals
Vegetation, Funghi & Lichen
Habitats

Study focus

Anthropogenic factors and impacts
Biodiversity (Species populations and Community Composition)
Ecosystem State, Structure and Functioning

Work steps

Presentation
Data collection
Plot establishment
Data analysis
Data archiving

Tool categories

TBD
Device
Software
Manual / Guideline / Framework

Tool purposes

Spatial Data
Numeric and Alphanumeric Data
Audio Data
Genetic Data
Photo/Video Data
Non Data generative
Chemical Compound Data