Sold by: MphasisÂ
Deployed on AWS
AI-powered system condenses DashCam footage into brief videos for streamlined accident insurance claims processing.
Overview
Accident Video Summarization AI streamlines insurance claim processing by condensing dash cam footage into concise videos. This AI-powered tool analyzes long recordings, extracting crucial moments and highlighting key events. By automating the summarization process, it improves accuracy and expedites claims handling. The system offers a side-by-side comparison of original and summarized videos, showcasing its effectiveness and facilitating swift decision-making for insurance claims. This innovative solution significantly reduces review time, enabling organizations to efficiently assess incidents and process claims more quickly.
Highlights
- Accident Video Summarization AI enhances insurance claim processing by creating concise videos from lengthy dash cam footage. It uses AI to pinpoint crucial moments, cutting review time significantly. This boosts efficiency and accuracy in claims handling, with comparative studies showing the system's effectiveness in facilitating swift decision-making.
- This system considers accident severity and context, capturing essential details in condensed videos. It analyzes patterns and key events to handle various accident scenarios. By extracting crucial moments, it enables insurance agents to swiftly evaluate summarized content, boosting overall workflow efficiency.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p3.8xlarge Inference (Batch) Recommended | Model inference on the ml.p3.8xlarge instance type, batch mode | $5.00 |
ml.p3.8xlarge Inference (Real-Time) Recommended | Model inference on the ml.p3.8xlarge instance type, real-time mode | $5.00 |
ml.g5.8xlarge Training Recommended | Algorithm training on the ml.g5.8xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $5.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $5.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $5.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $5.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $5.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $5.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $5.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
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Content disclaimer
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is version 1.0
Additional details
Inputs
- Summary
Inference: To use the trained model for summarizing videos: * Create a ZIP archive: You need to create a compressed file named Inference_Videos.zip. * Content of the ZIP archive: This ZIP archive should only contain the video files that you want the trained model to summarize. There's no need for any folders or additional files within the ZIP.
- Limitations for input type
- Inference: The ZIP archive must be named Inference_Videos.zip. The ZIP archive should only contain video files. No folders or other files are needed.
- Input MIME type
- application/zip, application/gzip
https://github.com/Mphasis-ML-Marketplace/AI-DashCam-Accident-Video-Summary/blob/main/Input/inference/Inference_Videos.zip
https://github.com/Mphasis-ML-Marketplace/AI-DashCam-Accident-Video-Summary/blob/main/Input/inference/Inference_Videos.zip
Resources
Vendor resources
Support
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