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This section covers Classification Configuration and how to access the data it provides.
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In order to become a track, an object needs to exhibit various attributes and behaviours, e.g. physical size, speed, directional change rate etc. Classification assumes different tracks would exhibit a certain set of attributes, and thus would be clearly different from other track types (see ATI.)
For example, people are smaller than vehicles. Vehicles travel faster than people. People can make quicker turns than vehicles, and so on. These attributes lead to the creation of a classification model. A model is created for each possible object type. The model lists likely values for each attribute (both a minimum and maximum, giving a range, or just a minimum). Each track generated within Witness 4.0™ is ‘scored’ against this model using those values.
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The properties are as follows:
Property | Description | Default Assessment |
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Size | The surface area of the target calculated using size in range x size in azimuth. | Does the (truncated) average size of the track across all sightings fall within the range of the model? |
Speed | The speed of the track. | Does the maximum speed seen across all sightings fall within the range of the model? |
Expected Speed | The typical speed expected of a track. This would normally be a smaller subset of the overall speed range. For example, a car can move as slowly as a walking person, however its expected speed would normally be much quicker. Similarly, whilst a vehicle may be able to travel at speeds from 0m/s to 44m/s (100 miles or 160km per hour) but in an urban road environment it’s more likely to be travelling from 4 to 14m/s (10mph to 30mph). | Does the (truncated) average speed of the track across all sightings fall within the expected speed range of the model? |
Acceleration | This is the change in speed between track sightings. | Does the maximum acceleration across all sightings fall within the range of the model? |
Weight | The total number of cells that make up the target. | Does the (truncated) average number of cells across all sightings fall within the range of the model? |
Strength | Sum of the signal strength for each cell which makes up the target. Note this value is only truly effective if using "Input" or "Level Above" for the thresholding process. All processed data exits thresholding with a value of 255. Therefore, Strength, by default, is weight multiplied by 255. | Does the (truncated) average signal strength across all samples fall within the range of the model? |
Seen Moving | This is the maximum speed that a track can travel before it is considered to be moving. This is used a base threshold to help distinguish between stationary debris and moving targets. | Does the average speed seen across all sightings exceed the value in the model? |
Direction Change | The absolute change in direction from one sighting to the next. For example, a person may change direction more quickly and more drastically than a vehicle, and so would have a higher minimum value. | Does the average change in direction across all sightings fall within the range of the model? |
Reset Trigger | A trigger which when its conditions are met will force the existing classification to be reset. This will result in the classification process starting again for this track. For example if a small, slow moving vehicle is incorrectly classified as a person you might want to force reclassification if the vehicle accelerates above normal walking speed. Clearly in this situation the track can't any longer be considered to be a person and reclassification should hopefully force it to change to a vehicle. |
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Each Classification Model by default is defined by these configurable properties except Debris, which by default is only concerned with Speed and Acceleration. |
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The categories are as follows:
Category | Default Properties |
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Speed and Acceleration | Expected Speed, Speed and Acceleration |
Weight and Strength | Weight and Strength |
Behaviour | Seen Moving and Direction Change |
Size | Size |
Each category and property is given a weighting. This can vary from model to model. These weightings will directly affect the model scoring. For example, if a target property matches a critical attribute then the score will be 50, whereas a matching optional property would only get 10 points.
Category | Score Weighting |
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Critical | x50 |
Desirable | x30 |
Optional | x10 |
Ignore | x0 |
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Ignore effectively ‘turns off’ certain attributes from having an effect on classification. |
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This can be achieved by Querying under the Playback tab:
Set a Track Query (for any classification).
In the Results Panel, the query results will be tabled. One column is labelled 'History'.
Select the rewind icon in the History column for the track you wish to analyse.
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A Track Details window will appear.
Select the Classification tab.
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A table of results will appear, listing the track's classification probabilities. These are calculated as a fraction of achieved points out of total points possible e.g. 10500/17000
In the above table, the track has been classified as a Vehicle, but has in an equal probability (70.6%) of being classified as a Large Vehicle.
These results merit classification adjustment, in order to differentiate between Vehicle classification and Large Vehicle classification. By modifying the classification, you can make a new set of point values, alongside the original score, for comparison.
Classification Adjustment
Firstly, analyse the individual cell results by hovering over them with the mouse pointer. Small boxes of information regarding the individual values of the different properties will appear.
In this example, the Weight classification has been highlighted. Note that the Value has fallen below the Vehicle classification Weight range (45-65>31.88), and thus the track is smaller and therefore not a Large Vehicle.
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Pull the Track Details window to one side, and select Classification Configs. Ensure the Config Function is enabled.
Select Edit, and then under Classification Models, adjust whichever value you see fit. In this example, the Vehicle: Weight and Strength: Weight range has been adjusted to 30-65.00.
When you select Save, a new tab will appear on the Track Details window: Modified Classification.
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A change in the Weight range has changed the score of the Vehicle classification, and raised the probability of the track being a Vehicle to 76.5%, clearly differentiating it from a Large Vehicle.
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There may be situations where a track doesn’t score highly at all in any model category. Regardless this track would still be classified according to the one with the highest score. To prevent this, the user is able to configure a percentage confidence rate to which the classification should aim, in the Advanced tab under the Configure Rules; https://navtechradar.atlassian.net/wiki/spaces/TUN/pages/211321151/ClearWay+Rules#Configuring-a-Rule window. You may set this to be very strict, however a satisfactory rate may be 70% |
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It may be necessary to create a new Classification Config for a system as the Default one is not editable. A new one can have it’s models settings tweaked and it is also possible to add / remove models.
To do this click, on Classification Configs in the Configuration Tree and select New:
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Then select Classification Config:
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This will open a Create New Classification Configuration wizard:
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Give your new Classification Config an appropriate name and click Finish:
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This will create a new Classification Config for you to modify. The Classification Models that were in the Default Classification Config have been replicated here, to allow you to tweak the settings, add or remove a model.
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To edit a model, select it and navigate through the settings and amend the ones required:
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By default, the Animal setting has not been enabled, so to enable it select the Enabled option:
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To add another model, if it is similar to another in size and weight, it will be easier to clone one of the current models by clicking the clone icon:
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Then select another Classification from the drop-down list:
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This will then display the new model:
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The settings can be adjusted to suit the new model:
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To remove a model from the list, click on it and then select the trash icon:
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Once you are happy with your modifications, click Save:
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The new Classification Config will now be displayed in the Configuration Tree:
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Applying a Classification Configuration
To change the Classification Configs from the Default to another:
Go to Topology, select the track engine and click Edit:
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Select the desired Classification Config from the drop-down list:
Then click Save:
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