Classification
Classification adds a semantic layer to objects being detected and tracked by the Qb. The resulting object class can be used to only generate alarms for certain types of objects (e.g., in object-based security zones).
See configuration: API definition.
Classification can be performed in two modes:
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Size-based classification (enabled by default, using a object-size-based heuristic)
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Model-based classification (using a machine learning model)
1. Size-based Classification
If no model is specified, each cluster is assigned to an object size category based on its bounding box surface area.
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The largest plane of the bounding box is used for size determination.
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Clusters smaller than the minimum threshold for SMALL objects are discarded.
- Minimum object surface area (unit: \(m^2\))
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The minimum bounding box surface area required for an object to be classified into a size category. These labels are later used for zone state computation (e.g., in object-based security zones).
| Category | Type | Default | Unit |
|---|---|---|---|
SMALL |
float |
0.01 |
\(m^2\) |
Lower size threshold for small objects. Objects smaller than this threshold are discarded. Valid range: [0.01 … 0.05]. |
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MEDIUM |
float |
0.16 |
\(m^2\) |
Lower size threshold for medium objects. This value has to be larger than the minimum size for small objects. Valid range: [0.05 … 0.5]. |
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LARGE |
float |
1.6 |
\(m^2\) |
Lower size threshold for large objects. This value has to be larger than the minimum size for medium objects. Valid range: [0.5 … 5.0]. |
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2. Model-based Classification
If a classification model is specified, size-based labeling is replaced by machine learning-based classification.
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A pre-trained support vector machine (SVM) model is used.
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For each cluster, a specific feature vector is computed by deriving the objects volume, intensity or point distribution.
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The feature vector is passed to the model.
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Result of the classification is stored in Detected Object.
Limitations
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Size-based classification
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Only considers the largest plane of the bounding box; shape and orientation details are ignored.
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Fixed thresholds may not generalize across different sensor setups or environments.
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Model-based classification
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Requires a pre-trained model; on-device training is not supported.
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Classification quality strongly depends on the chosen features and training dataset.
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Only predefined object classes are supported.
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