Implementing Masked Video Segmentation with Object Detection - GroundingSAM with Overeasy
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Feature Request: Implementing Masked Video Segmentation with Object Detection - GroundingSAM with Overeasy
Description:
I would like to request the integration of masked segmentation from the Grounding SAM project (https://github.com/IDEA-Research/Grounded-Segment-Anything) with the Overeasy model (https://github.com/overeasy-sh/overeasy). This integration intends to create a feature that performs masked video segmentation and outputs object detection within bounding boxes. This functionality will enhance the application by extending it from image to video and advanced video analysis capabilities.
Use Case:
Users who require precise object detection and segmentation in video content will benefit from this feature, like applications in surveillance, automated video editing, and masked video analysis.
Benefits:
- Extending image to video analysis of this model
- Enhanced Video Analysis and improved Efficiency.
- Combining the strengths of Grounding SAM and Overeasy models for a robust solution.
Develop a pipeline that first segments objects in each video frame using Grounding SAM, and then applies the Overeasy model to detect and label these objects within the segmented masks.
Dataset: Use a diverse video dataset to test the integrated feature, ensuring it works across various scenarios and object types.
Performance Metrics :Evaluate the accuracy of segmentation and object detection, as well as the processing time for each video.
User Feedback: Collect feedback from users to refine and improve the feature based on practical use cases.
This is an interesting suggestion that combines two powerful models to create an advanced video analysis pipeline. This feature could indeed provide significant benefits for applications requiring detailed video analysis. The combination of precise segmentation from GroundingSAM with the object detection capabilities of Overeasy could yield powerful results.