What is the Logo Detection?
Efficiently Identify Logos in Video Content with AI-Powered Detection
Module Description:
The Logo Detection module in DeepVA is designed to automatically recognize logos appearing in video content. This feature is essential for industries such as media monitoring, brand management, and sponsorship tracking, where accurate identification of logos in video streams is crucial.
The AI-driven system uses advanced object detection techniques to scan each frame of the video, identify logos, and extract metadata, which helps users keep track of brand appearances and sponsorships in real-time or recorded media.
Alpha Release:
This module is currently in its early development stage. We recommend thoroughly testing it with your specific use case to ensure accurate results. Adjusting the configuration based on your data will help you fine-tune the performance. Keep in mind that since it's an alpha release, some features may still be experimental, so regular monitoring and adjustments may be necessary to achieve optimal results.
How It Works:
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Select the Media File:
Begin by choosing the media file you want to analyze for logo detection. -
Activate the Logo Detection Module:
In the left-hand column, select the Logo Detection module and configure the parameters according to your needs. -
Define Parameters:
Set up the detection parameters, such as the confidence threshold and whether to only return known predictions. Then, click the yellow Add Module button. -
Start the Analysis:
You can either add more modules or directly start the analysis by clicking "Start Analysis".
Parameters Available:
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Model (Dropdown):
Select the specific logo detection model to be used during the analysis. -
Min. Confidence (0-100):
This parameter sets the minimum confidence level for predictions. The module will only return results that are above this threshold. -
Only Known Predictions (Checkbox):
If enabled, the system will only return bounding boxes for known logos. -
Min. Detection Confidence (0-100):
This parameter ensures that the bounding boxes displayed are only for logos detected with a confidence level higher than the specified threshold.
Displaying the Results:
After running the analysis, the system displays the results in a user-friendly viewer that includes:
Timeline:
The timeline, located below the player, displays the entire video runtime and the results from each module as gray bars. By clicking on any of the grey result bars, you will see details such as:
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- Label Name
- Confidence Value (0%-100%)
- Timecode (TC)
- Exact frame numbers
- Runtime/Duration
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Search Field:
The Logo Detection does not have a search field.
Result Cards:
Each result card provides the Logo Label and by clicking brings you to the detected logo.Module Section:
On the right side of the player, you can open the Module Section to adjust parameters or troubleshoot specific results. This section provides detailed metadata about each logo detected in the video and allows you to fine-tune future analyses.Practical Use Cases:
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Brand Management:
Companies use logo detection to monitor where and how often their brand appears in media, ensuring proper representation and visibility. -
Sponsorship Tracking:
Sports teams and event organizers use logo detection to track sponsorship placements across multiple videos and broadcasts, ensuring that sponsors receive the agreed-upon screen time.
Bounding Boxes:
Bounding boxes are the rectangular overlays around detected logos, highlighting exactly where in the frame the logo is located. They can be activated for the Logo Detection in the top menu bar.
Troubleshooting
To investigate problems with your Logo Recognition results, follow these steps
In the Module section on the right side of the Results Viewer, you can access detailed parameters for each module. These parameters can provide additional information that can help you troubleshoot by allowing you to verify settings or fine-tune module performance.
Adjust confidence levels: Increase or decrease the confidence level to get more matches or fewer false positives.
Apply a custom logo model: If the labels don't match your expectations, try uploading a custom model with the Deep Model Customizer for your use case.