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Depth perception for Autonomous Vehicles

Cuboids on images can be used for recognizing vehicles, analyze traffic signals with real-time trajectory data and calculate average speed at different locations, average stop times at intersection ingresses. Improve state of transportation for pedestrians, cyclists & obstacles on the road.

Depth perception for Autonomous Vehicles

Depth perception for Autonomous Vehicles

Annotations

2D Cuboids

Classes

  • Construction Vehicle
  • Bicycle
  • Pedestrian
  • Motorcycle
  • Vehicle
Depth perception for Autonomous Vehicles

Depth perception for Autonomous Vehicles

Annotations

2D Cuboids

Classes

  • Construction Vehicle
  • Bicycle
  • Pedestrian
  • Motorcycle
  • Vehicle

We're trusted by

We were very impressed with Playment’s ability to grasp complex requirements and quickly build custom tools to support it. Jitesh(Engagement Manager) was very helpful with sharing his domain expertise to formulate the solution.
Nikola Noxon
Senior Engineer, Daimler
Playment's fully managed approach has been critical in factoring the variability and scaling up our annotation requirements. Thanks to their tools and skilled workforce, we are extremely satisfied by the quality and turnaround time they have provided. I would highly recommend Playment.
Shmoolik Mangan
Algorithm Development Manager, Vayavision

How It Works?

Project Setup

Our data labeling platform enables project manager to train real people annotate your data and to make sure your models are being trained on high-quality data.

Raw Dataset

Send us dataset and task guidelines. We support API, .CSV, FTP, cloud etc. to source data and setup tasks.

Project Setup

Our data labeling platform enables project manager to train real people annotate your data and to make sure your models are being trained on high-quality data.

Human in the Loop

The semi-automatic labeling process involves combination of deep learning models, hueristics and manual human edits to create high quality annotation at scale.

Rigorous QC

We execute multi-level automated and manual quality checks over each and every annotation. Machine checks to eliminate random or systematic errors combined with human consensus models ensures highly accurate output.

Export Results

Collect all the ground truth dataset you need to train your model.

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