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The biggest challenge in crowd analytics is working with dense crowds where perspective and occlusion errors can hurt the performance and accuracy of the calculated metrics.
The client had the requirement for a system that would provide them real-time analytics of large crowds on a single dashboard, the purpose of this was to prevent riots and stampedes in highly crowded areas. The client wanted counts of individuals, flow of individuals per minute and their direction of movement in a specific zone. If an individual was to walk against the general flow of the crowd a real-time alert was to be generated. This system required both software development and R&D towards the Artificial Intelligence side from ByteCorp.
We used the clients existing data along with open source datasets and custom AI algorithms to create the system that would adhere to the client's requirements with up to par performance.
The benefits of the system for the client was in detection of possible situations that could lead to stampedes causing deaths, as well as cutting down on the manual QA costs that existed in the current system.
ByteCorp was involved with the client from the initial process including identification of requirements to the finalisation of the dashboards designs, an R&D cycle was conducted by the ByteCorp team to create effective AI Algorithms. The effectiveness of the system was measured against criteria set out by the client.
Let's walk you through the project and explore how we could create something similar for your business.
Prefer email? Head of Growth
Crowd Analysis is a use case of Deep Computer Vision where statistics and analytical inferences are generated by visualing crowds in videos and images, these analytics can include crowd counting, crowd flow estimation, crowd direction prediction, and person reidentification.
Let's walk you through the project and explore how we could create something similar for your business.
Prefer email? Head of Growth
The biggest challenge in crowd analytics is working with dense crowds where perspective and occlusion errors can hurt the performance and accuracy of the calculated metrics.
The client had the requirement for a system that would provide them real-time analytics of large crowds on a single dashboard, the purpose of this was to prevent riots and stampedes in highly crowded areas. The client wanted counts of individuals, flow of individuals per minute and their direction of movement in a specific zone. If an individual was to walk against the general flow of the crowd a real-time alert was to be generated. This system required both software development and R&D towards the Artificial Intelligence side from ByteCorp.
We used the clients existing data along with open source datasets and custom AI algorithms to create the system that would adhere to the client's requirements with up to par performance.
The benefits of the system for the client was in detection of possible situations that could lead to stampedes causing deaths, as well as cutting down on the manual QA costs that existed in the current system.
ByteCorp was involved with the client from the initial process including identification of requirements to the finalisation of the dashboards designs, an R&D cycle was conducted by the ByteCorp team to create effective AI Algorithms. The effectiveness of the system was measured against criteria set out by the client.
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