KotonGenderDetect(Door) Computer Vision Project
Updated 2 years ago
Here are a few use cases for this project:
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Gender-Based Traffic Analysis: The "KotonGenderDetect(Door)" could be used in commercial settings like malls, department stores, or events to analyze the gender-based customer flow. Companies can use this data to tailor their promotions and layout based on the predominant gender of visitors at certain times or days.
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Security and Surveillance: This computer vision model can aid in security surveillance, for instance, in identifying and tracking individuals in crowded public spaces. It can quickly distinguish gender and head specifics, useful for identifying suspects in ongoing investigations or monitor areas with gender-specific access.
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Targeted Marketing Campaigns: Retailers or advertisers can use this model to analyze the gender demographic of passers-by in a certain area, helping them design and implement marketing campaigns or digital signage targeted at men or women specifically.
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Spatial Planning: Urban planners can use the data generated by this model to understand the gender ratio of visitors to different public spaces (parks, malls, etc.). This information can be used to plan routes, public amenities, and other features based on the gathered data.
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Emergency Response and Evacuation Planning: In emergency situations, understanding the composition of a crowd can be critical in planning effective evacuations. This model could provide valuable data for first responders to better tailor their strategies in such scenarios.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
kotongenderdetect-door_dataset,
title = { KotonGenderDetect(Door) Dataset },
type = { Open Source Dataset },
author = { Enes Yldz },
howpublished = { \url{ https://universe.roboflow.com/enes-yldz/kotongenderdetect-door } },
url = { https://universe.roboflow.com/enes-yldz/kotongenderdetect-door },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { mar },
note = { visited on 2025-02-16 },
}