Market_ceva Computer Vision Project

SVACeva

Updated 2 years ago

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Classes (5)
cutii
mancare
oameni
plante
reclame

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Description

Here are a few use cases for this project:

  1. Retail Analysis: The "Market_ceva" model can be used by retailers to analyze the patterns of people's movement through the store, their interactions with products (mancare, cutii), their engagement with in-store adverts (reclame), and how they relate to store layout including plants (plante). Such valuable insights can help improve product placement and store design.

  2. Smart Surveillance Systems: The model can help monitor public spaces or shops by efficiently identifying human activity, distinguishing between people (oameni), food items (mancare), boxes (cutii), plants (plante), and advertisements (reclame). This information can be used to help improve security and prevent theft.

  3. Outdoor Advertising Effectiveness Evaluation: Companies can use the model to evaluate the effectiveness of their outdoor advertising campaigns (reclame) by analyzing and estimating the number of people who notice their ads.

  4. Urban Planning and Development: The model could be aids urban planners and architects by providing insights about how people interact with urban spaces, including pedestrian movements, areas where people are frequently eating (mancare), popular advertising spaces (reclame), and aesthetic preferences such as the placement of plants (plante) and boxes (cutii).

  5. Crowd Management: In densely populated urban areas or during events, the "Market_ceva" model could assist in managing crowds, identifying emergent situations, detecting anomalies, and ensuring safety rules are followed.

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            market_ceva_dataset,
                            title = { Market_ceva Dataset },
                            type = { Open Source Dataset },
                            author = { SVACeva },
                            howpublished = { \url{ https://universe.roboflow.com/svaceva/market_ceva } },
                            url = { https://universe.roboflow.com/svaceva/market_ceva },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-11-30 },
                            }
                        
                    

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