Register for the Ramblr Demo Environment
Sign-up for the Ramblr Demo Environment to gain access and view the annotated videos from the Ramblr KPI Benchmark Study. The Ramblr Demo Environment showcases our unique visualization browser to curate and view annotated video data.
What we Offer
Ramblr will collect and annotate your data and work hand-in-hand with you to evaluate, improve, and launch your models into production.
Privacy-preserving, secure data collection of high-quality egocentric data. We manage, organize, and deliver your data collection project according to your requirements.
Scalable AI-powered video annotation to generate accurate and consistent instance segmentation masks in accordance with your specifications.
Cloud-native infrastructure to manage, curate, and validate large datasets in preparation for the training of your Machine Learning models.
Data protection and privacy from collection to ingestion to the removal of personally identifiable information (PII) right through to data storage and transfer.
Ramblr’s data engine generates consistent spatiotemporal segmentation masks and semantic labels for all context relevant objects.
Multi-modal Data Collection
We support various sensor sources. Sensor data includes RGB and monochrome cameras along with IMU, audio, eye tracking and more. Recorded data, metadata, and sensor streams can be accessed with Ramblr’s data browser. We are agnostic to data collection devices, so bring your own or ask us for recommendations.
AI-guided Video Annotation
Browser-based and purpose-built to efficiently annotate egocentric video data. We use state-of-the-art ML models and egocentric data specific signals to detect and track context-relevant objects of interest in the video and for AI-assisted annotation. We identify representative keyframes and propagate the segmentation masks and semantic labels throughout the video, reducing annotation time significantly.
Automated QA Process
To ensure consistent and high-quality annotations throughout the video, our quality assurance (QA) model automatically detects category inconsistencies, object mask shape anomalies, and missing object masks. The system tracks objects across all frames using globally unique object identifiers while also tracking reoccurring objects and detecting new, relevant objects. Human annotators correct the detected issues, and we propagate the corrected masks, reducing annotation efforts without sacrificing accuracy.
Gain insights, manage, explore, and search your datasets. Curate and validate your high-quality annotated data and access relevant metadata. With Ramblr’s data browser you can speed up your data-centric processes and visualize the high-quality data that production-ready models need.
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Pipeline and Tools
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Multi-sensor Data for Egocentric AI
Data captured from a first-person view (aka “egocentric data”) enables a multitude of Augmented Reality applications. Collecting, processing, segmenting, and annotating egocentric data poses unique challenges, including the handling of large datasets capturing a wide variety of use cases. At Ramblr, we are building a full service egocentric data pipeline and AI-assisted tools to address the unique challenges of Egocentric AI.
Ramblr 1.0 - Enabling AI systems to see the world like humans do.
With Ramblr 1.0, we enable companies and academic institutions to collect, segment, and annotate large multi-sensor datasets in a cost-efficient way. Our scalable and privacy-preserving platform as well as our efficient AI-assisted tools help annotators, data scientists, and application developers to deploy Augmented Reality applications faster.
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