Points Covered :
- The Manual Challenge of Identifying, Sorting and Distributing Marathon Event Photographs
- Automating the Process using Machine Learning
- The Technology Stack Used
- Value Delivered
The Manual Challenge: Identifying and Distributing Marathon Event Photographs :
In the context of marathons and large-scale events, a significant challenge arises in efficiently identifying and distributing event photographs to their respective participants. Currently, this process is predominantly manual and lacks the speed, accuracy, and convenience required for a seamless experience. This case study aims to delve into the existing problem and explore potential solutions.
Consider a large-scale marathon event with thousands of participants. After the event, a volunteer team is tasked with distributing race day photos to the runners. They spend hours manually scanning each image, searching for runners’ bib numbers or recognizable faces, and then uploading these photos to individual runners’ profiles or sending them via email. This process not only consumes valuable time and resources but is also prone to errors, as volunteers may miss or misidentify participants. As a result, the current approach hampers the overall experience for both runners and event organizers.
Addressing this problem requires an efficient, automated solution to streamline image identification and distribution during marathons, enhancing participant satisfaction and event management.
Automating Marathon Event Photograph Handling
To tackle the challenges posed by the manual identification and distribution of marathon event photographs, we have developed an efficient Machine Learning Pipeline. This automated system streamlines the process, ensuring that event participants receive their photos accurately and promptly. Here’s a brief overview of the workflow:
- Image Collection: Our system collects event photographs from photographers or providers, centralizing the image repository for further processing.
- Machine Learning Pipeline: The heart of our solution is a robust Machine Learning Pipeline. This pipeline performs several critical tasks:
a. Image Sorting: Using object recognition algorithms, the system identifies participants in the images, focusing on race bib numbers as the primary identifier.For instance, it recognizes that a runner with bib number “1234” is present in multiple photos.
b. Database Integration: The sorted images are organized based on bib numbers, creating tags for each participant. The system then cross-references these bib numbers with a user database to match individuals with their respective images. For eg : The system accesses a user database, matching bib number “1234” to a participant named “Sarah Smith.” All images associated with bib number “1234” are automatically tagged as “Sarah Smith’s photos.” - Image Distribution: With the matches established, the system seamlessly delivers the photos to participants. Users receive their images through email, a dedicated platform, or a secure link, ensuring a hassle-free experience.Like in our case, Sarah Smith receives an email notification within hours of the marathon’s conclusion. The email contains a link to a personalized gallery where she can easily access and download her event photos.
Technology Stack
Our solution leverages a cutting-edge technology stack to automate marathon event photo handling efficiently. We utilize Python, Databricks, TensorFlow, and Keras to create a seamless experience for event participants. Here’s a breakdown of our technology approach:
- Python: Python serves as the backbone of our system, facilitating data processing, model training, and deployment.
- Databricks: Databricks provides a robust data processing environment, enabling efficient data ingestion, transformation, and analysis.
- TensorFlow and Keras: These deep learning frameworks power our machine learning models, allowing us to develop and train complex neural networks for image processing.
Machine Learning Pipelines:
We’ve designed two specialized pipelines to address the challenge of bib detection:
- Object Detection Pipeline: We employ state-of-the-art models like SSD Resnet FPN V1 and Yolo v6 for bib detection. These models locate bibs in images, marking the regions of interest.
- Image Classification Pipeline: After bib detection, we use image classification models, including VGG-16, to extract bib numbers from the identified regions.
Transfer learning and extensive optimization have enabled us to achieve an impressive accuracy rate of 92%.
Edge Device Deployment :
Furthermore, we ensure the scalability of our solution by converting the model into a TensorFlow Lite format, making it ready for deployment on edge devices. This innovation extends the reach of our system, enabling quick and efficient photo processing even in resource-constrained environments.
Our technology stack and pipelines collectively contribute to a streamlined and error-free way for efficiently identifying and distributing event photographs to the respective participants.
Delivering Value Through Automation:
By automating the labor-intensive and error-prone process of identifying and distributing event photos, we revolutionize the event experience.Participants benefit from a seamless and swift photo retrieval process, enhancing their post-event satisfaction and engagement.
Through a sophisticated technology stack, machine learning pipelines, and edge device deployment, our solution ensures the highest accuracy in bib detection and image processing. Ultimately, we bring automation to the forefront, transforming marathon event photography into a reliable, efficient, and memorable service.
