Design And Development OF EngageVision Pro

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  Project Thumnail

Inspiration

I was inspired to develop EngageVision Pro after witnessing the significant rise in demand for data scientists over the past 2 to 3 years. This observation led me to question the increasing value placed on data analysis and data scientists. Upon reflection, I realized the immense importance of data in today's world. This realization was further reinforced by the prevalence of data leaks, particularly in the realm of vision, especially concerning CCTV systems. This insight motivated me to create EngageVision Pro to address the critical need for efficient data analysis in visual data domains.

What it does

EngageVision Pro is an innovative system that uses computer vision to track customer engagement with products in retail environments. It analyzes CCTV feeds to detect when customers are interacting with products and provides detailed reports on engagement time. The best thing is I added an upload option, so the retailers can upload past videos to get their analysis. And data is also integrated with Snowflake, enabling retailers to analyze insights and make data-driven decisions using Snowflake Streamlit.

How I built it

I built EngageVision Pro using a combination of Python, OpenCV, and the YOLOv8 model for object detection. I developed a user-friendly interface for easy setup and customization using Streamlit. The system is designed to integrate with various CCTV systems, ensuring compatibility with different retail environments. Finally implemented a database to store the data from the camera in the snowflake.

Challenges I ran into

One of the significant challenges I faced was optimizing the YOLOv8 model for real-time detection. I had to fine-tune the model to achieve high accuracy while maintaining fast processing speeds. Another challenge was ensuring seamless integration with different CCTV systems and Snowflake. And the big challenge was the data from the camera, just give an example the system gets 25+ data/sec according to the frame rate. I overcame these challenges through rigorous testing and collaboration with experts in computer vision and data integration.

Accomplishments that I have proud of

I proud of developing a system that can accurately track customer engagement in real-time, providing retailers with valuable insights to improve their sales strategies. I also proud of the user-friendly interface, which makes it easy for retailers to set up and use the system. The integration with Snowflake Streamlit is another significant accomplishment, as it enables retailers to easily analyze engagement data and make data-driven decisions.

What I learned

Through this project, I learned the importance of fine-tuning AI models for specific use cases. I also gained experience in integrating AI systems with existing infrastructure, such as IP cameras systems and databases. Additionally, I learned about the importance of user experience in making complex technology accessible to a wide range of users.

What's next for the Design And Development OF EngageVision Pro

My next steps for EngageVision Pro include expanding its capabilities to track customer demographics and sentiment analysis. I also plan to explore partnerships with retail technology companies to distribute the system more widely. Furthermore, I aim to continue refining the system based on customer feedback, ensuring that it remains a valuable tool for retailers seeking to understand customer engagement.

Built With

• analysis
• data
• dataanalytics
• deeplearning
• opencv
• processing
• python
• snowflake