Overview
The "Perspective Zones" project aimed to improve the user interface for AI engineers using Unleash live Cloud product. Zones are designated areas within a visual field (e.g. image or video feed) used for marking and analysing specific objects and/or measurements, often employed in AI applications for tasks such as depth estimation and object size calculation.
The Challenge
The project was initiated to address the inefficiencies in the existing Zone UI, which hindered user engagement and productivity. The business goal was to enhance operational efficiency by providing a user-friendly tool that integrates seamlessly with existing systems. The project focused on updating UI and adding a new type of zone, "Perspective Zones," to eliminate the need for an external tool and streamline the process of marking up perspective zones.
The project was initiated to address the inefficiencies in the existing Zone UI, which hindered user engagement and productivity. The business goal was to enhance operational efficiency by providing a user-friendly tool that integrates seamlessly with existing systems. The project focused on updating UI and adding a new type of zone, "Perspective Zones," to eliminate the need for an external tool and streamline the process of marking up perspective zones.
Requirements
The primary objective was to design a user-friendly interface for marking up perspective zones, thus eliminating the need for external tools. The solution needed to allow users to:
- Mark-up on image areas for calculating speed and size of detections.
- Draw perspective zones outside of images.
- Input measurements in centimeters.
- Operate within the constraint of one perspective zone per device.
- Mark-up on image areas for calculating speed and size of detections.
- Draw perspective zones outside of images.
- Input measurements in centimeters.
- Operate within the constraint of one perspective zone per device.
Success Metrics
Success was measured by the reduction in reliance on external tools, improved user satisfaction, and enhanced onboarding efficiency for new team members.
Foundational research: mapping internal process of AI project delivery.
Research
Research Goals and Methods
Research involved understanding user needs, identifying pain points with the existing Zone UI, and gathering feedback through user interviews and benchmarking tests. The key findings indicated that the existing UI was hard to understand and use.
Research involved understanding user needs, identifying pain points with the existing Zone UI, and gathering feedback through user interviews and benchmarking tests. The key findings indicated that the existing UI was hard to understand and use.
Problem
The main issue was the complexity and unreliability of the existing Zone UI, which led to user frustration and avoidance. The existing Zone UI had numerous bugs and a steep learning curve that even experienced users found challenging.
The main issue was the complexity and unreliability of the existing Zone UI, which led to user frustration and avoidance. The existing Zone UI had numerous bugs and a steep learning curve that even experienced users found challenging.
Existing UI with wireframes showing potential selection.
Design Process
Approach
The design process involved iterative prototyping and user testing. I designed and run collaborative workshops with AI engineers to ensure the design met technical requirements and user needs. The project aimed to create a more intuitive and reliable interface.
The design process involved iterative prototyping and user testing. I designed and run collaborative workshops with AI engineers to ensure the design met technical requirements and user needs. The project aimed to create a more intuitive and reliable interface.
Design Output
The final design allowed users to easily draw and adjust perspective zones, input precise measurements, and integrate seamlessly with AI applications. The design eliminated the need for a cumbersome process involving external tools.
The final design allowed users to easily draw and adjust perspective zones, input precise measurements, and integrate seamlessly with AI applications. The design eliminated the need for a cumbersome process involving external tools.
Adding Perspective Zone to area of interest in mining crusher.
Outcome
The implementation of "Perspective Zones" led to significant improvements:
1. AI team no longer needed to use external tools, streamlining their workflow.
2. The new tool facilitated easier onboarding of new team members by providing a clear and intuitive interface.
3. Customers in industries such as mining and transport gained a valuable tool to improve operational efficiency and cost savings by leveraging existing CCTV systems for object measurement.
1. AI team no longer needed to use external tools, streamlining their workflow.
2. The new tool facilitated easier onboarding of new team members by providing a clear and intuitive interface.
3. Customers in industries such as mining and transport gained a valuable tool to improve operational efficiency and cost savings by leveraging existing CCTV systems for object measurement.
Business Value
The project contributed to cost savings and increased productivity by reducing maintenance downtime and optimizing daily operations.
Reflections and Learnings
The project highlighted the importance of user-centered design in creating effective and efficient tools. By focusing on user needs and technical constraints, the "Perspective Zones" project successfully addressed key challenges and delivered a valuable solution.
Future Improvements
Future iterations could explore additional features such as multi-zone support and enhanced analytics integration to further enhance the tool's capabilities.
Future iterations could explore additional features such as multi-zone support and enhanced analytics integration to further enhance the tool's capabilities.