Feb 22, 2024
No image
Completed
Computer Vision Solution for Effective Advertising Placement
$100,000+
7-12 months
United Kingdom
2-5
Service categories
Service Lines
Artificial Intelligence
Web Development
Domain focus
Commerce
Programming language
Python
Challenge
The fundamental idea was to create a device harnessing AI technologies for analyzing captured images, discerning the audience’s average attributes, and enabling the presentation of targeted advertising.
Related objectives:
1) Deploy the computer vision model on edge devices with limited GPU and RAM;
2) Train the AI computer vision model with labeled data.
The fundamental idea was to create a device harnessing AI technologies for analyzing captured images, discerning the audience’s average attributes, and enabling the presentation of targeted advertising.
Related objectives:
1) Deploy the computer vision model on edge devices with limited GPU and RAM;
2) Train the AI computer vision model with labeled data.
Solution
In collaboration with the client’s team, Timspark created a compact on-premises computer vision device that can capture and analyze visual data.
1) Machine learning model training for successful object recognition:
the team developed and fine-tuned the classifier model to meet the project’s unique requirements. The model analyzes received images, determines the average class among all attributes of captured objects, and subsequently identifies the target audience for the advertisement.
2) Deployment on Jetson Nano and Jetson Xavier:
to address challenges linked to the limited memory and slow data processing, the model was transformed into ONNX and TensorRT formats, ensuring its seamless deployment on edge devices such as Jetson Nano and Jetson Xavier.
In collaboration with the client’s team, Timspark created a compact on-premises computer vision device that can capture and analyze visual data.
1) Machine learning model training for successful object recognition:
the team developed and fine-tuned the classifier model to meet the project’s unique requirements. The model analyzes received images, determines the average class among all attributes of captured objects, and subsequently identifies the target audience for the advertisement.
2) Deployment on Jetson Nano and Jetson Xavier:
to address challenges linked to the limited memory and slow data processing, the model was transformed into ONNX and TensorRT formats, ensuring its seamless deployment on edge devices such as Jetson Nano and Jetson Xavier.
Results
Our specialists successfully developed a classifying model that detects and tracks individuals within the camera’s field of view. The model analyzes the captured visual data, accurately identifying selected class among all attributes, and displays targeted advertisements on nearby screens in accordance with the specified class request.
Benefits for client:
the client successfully applies the computer vision software to show the advertisement effectively to the relevant audience. Based on the successful results, we are going to improve the model further in our ongoing collaboration.
Our specialists successfully developed a classifying model that detects and tracks individuals within the camera’s field of view. The model analyzes the captured visual data, accurately identifying selected class among all attributes, and displays targeted advertisements on nearby screens in accordance with the specified class request.
Benefits for client:
the client successfully applies the computer vision software to show the advertisement effectively to the relevant audience. Based on the successful results, we are going to improve the model further in our ongoing collaboration.