Introduction

This section covers Classification Configuration and how to access the data it provides.

Contents

Overview

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 https://navtechradar.atlassian.net/wiki/spaces/TUN/pages/243105859.)

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.

This score is calculated by determining if certain attributes are within a specified range. For each attribute that is within the range, that model gets 1 attribute point. Each attribute point is then weighted according to how important that attribute is to the model. This gives each track a score. The default classification uses 8 tracks (or samples), so a track’s final score is given as a percentage of achieved score against possible total score (over those 8 tracks).

Whichever model ultimately scores higher is deemed the correct classification for that track.

Classification Config

This is essentially the folder that encompasses all the classification models. Multiple folders can exist, each with different configurations for the models they contain.

Classification Name: The name of this set of classification configurations. There will be a default folder named 'Default'.

Samples: The track count used to create a mean track final score before the track is classified.

Min. Probability Required (%): This is the minimum level of confidence required before you consider the track to have been successfully classified. If this confidence level is not met then you can specify that the system continues for a further n sightings. The value of n is supplied by the next parameter, Extra Sample Increment.

Extra Sample Increment: This value is used in conjunction with Min. Probability Required. If the probability level is not met then the system will continue to classify for a limited number of additional sightings. This number is specified by the Extra Sample Increment value. If at the end of this additional sample period the classification has a confidence level higher than Min. Probability Required then the track will be successfully classified. However, if it falls below this threshold then the track will remain unclassified for as long as its tracked. The default Extra Sample Increment value is 4, giving a potential sample period of 12 (8 + 4).

Classification Models

The Classification models for tracks are:

Open

Classification Properties

Each target (or track) is assessed based on properties provided by the radar and tracking software. These properties are defined by:

Range:

Min: This is the minimum value that the model is looking for from the summary sample data. If the summary data from the analysed track is greater than or equal to this value then it will have met the minimum criteria. Where there is no maximum value provided, see below, then this is the only criteria that needs to be met. If it is satisfied then the track is awarded the appropriate weighted points for this classification model, increasing the likelihood that the track is a match.

Max: This is the maximum value that the value is looking for from the summary sample data. If the summary data from the analysed track is less than or equal to this value then it will have met the maximum criteria. Note that this value is optional. If omitted then the model will assume any value above the minimum (see above) is a match. If a minimum and maximum are specified then both criteria must be met for the track to match this attribute of the model.

Summary Method: This specifies the method for summarising the recorded samples. All track sightings are processed, up to the sample count, and then each of the properties is summarised using this specified method before being compared against the model.

Min: This method means the lowest value seen on the track during the sampling period will be used to compare against the model.

Max: This methods means the highest value seen on the track during the sampling period will be used to compare against the model.

Mean: This method means the average of all values seen on the track during the sampling period will be used to compare against the model.

Truncated Mean: This method means that the average of all values (minus the outliers) seen on the track during the sampling period will be used to compare against the model. The truncated mean will sort the sample data and then remove a percentage of the values from the top and bottom of the sorted list. So the amount of samples ignored at the top and the bottom is % / 2. For example if you had sample data that looked like this 3,7,21,34,36,37,55,89 (8 samples) and the truncated percent was 25% then we would lose 2 of our 8 values (25%) but 1 from the top and 1 from the bottom. So the final value would be the average of the 6 remaining values (i.e. 7,21,34,36,37,55 - average is 190/6 = 31.7).

The properties are as follows:

Category and Property Weighting

Each track property is group into a category. This is so each category can be weighted on a model by model to ensure the scoring system is biased towards the key attributes for a specific classification.

The categories are as follows:

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.

Analysing Tracks in Playback

Each model can be fine-tuned by adjusting the properties. The key concept is to analyse tracks in Playback and review the typical values associated with each type (i.e. person vs vehicle) and then adjust the property values in the classification configuration to match.

This can be achieved by Querying under the Playback tab:

1. Set a Track Query (for any classification).

2. In the Results Panel, the query results will be tabled. One column is labelled 'History'.

3. Select the rewind icon in the History column for the track you wish to analyse.
   
   

   Open

   

    

4. A Track Details window will appear.

5. Select the Classification tab.
   
   

    

6. 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

Classification Adjustment

1. 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.

2. 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.
   
   

    

3. Pull the Track Details window to one side, and select Classification Configs. Ensure the Config Function is enabled.

4. 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.
   
   

5. When you select Save, a new tab will appear on the Track Details window: Modified Classification.
   
   

    

6. 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.

Creating a Classification Configuration

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.

1. To do this click, on Classification Configs in the Configuration Tree and select New:
   
   

    

2. Then select Classification Config:
   
   

    

3. This will open a Create New Classification Configuration wizard:
   
   

    

4. Give your new Classification Config an appropriate name and click Finish:
   
   

    

5. 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.
   
   

    

6. To edit a model, select it and navigate through the settings and amend the ones required:
   
   

    

7. By default, the Animal setting has not been enabled, so to enable it select the Enabled option:
   
   

    

8. 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:
   
   

    

9. Then select another Classification from the drop-down list:
   
   

    

10. This will then display the new model:
    
    

     

11. The settings can be adjusted to suit the new model:
    
    

     

12. To remove a model from the list, click on it and then select the trash icon:
    
    

     

13. Once you are happy with your modifications, click Save:
    
    

     

14. The new Classification Config will now be displayed in the Configuration Tree:
    
    

Applying a Classification Configuration

To change the Classification Configs from the Default to another:

1. Go to Topology, select the track engine and click Edit:
   
   

    

2. Select the desired Classification Config from the drop-down list:
   
   

    

3. Then click Save:
   
   

Related Information