Dynamic subtraction

Aim

To detect the subject, while compensating for temporal changes in the background.

examples

▪Detect a rat in a water maze, where other detection methods do not work, usually because of the effect of waves in the pool.

▪Detect a mouse in a PhenoTyper or home cage with bedding material, where the animal’s activity (e.g. digging) changes the appearance of the background.

▪All cases where lighting changes slowly.

How Dynamic Subtraction works

Like with Static subtraction, EthoVision XT compares each sampled image with a reference image, with the important difference that the reference image is updated regularly. This compensates for temporal changes in the background. See How the reference image is updated in Dynamic subtraction.

Procedure

1.In Detection Settings pane, click Advanced, then Method. Select Dynamic subtraction.

2.Click the Background button. The Reference Image window opens with the image that is currently used as background. The aim is to obtain a reference image that does not contain images of the animals you want to track. To do so, follow the instructions on the screen in consecutive order. If A fails, move on to B, if that fails move on to C. See Optimize the reference image.

3.From the Subject color … list, select one of the options from the list, depending on the color of the subject you want to track.

4.Move the slider next to Bright/Dark to select the range of the contrast between subject and background.

 

5.Move the slider next to Frame weight or enter the value in the appropriate field to specify how the reference image is updated (range 0-100%):

 

In typical situations, a value between 1-5 gives a good result.

important As much of the animal's body must be detected for good tracking. See Advanced detection settings: Subject contour to optimize body detection.

Frame weight

▪Select a low value if you want to have a large number of past images to contribute to each reference image. As a result, changes in the background are diluted over many images. Choose a low value when the background changes slowly.

▪Select a high value if you want to have a small number of past images to contribute to each reference image. As a result, changes in the background are captured over short time. Choose a high value when the background changes rapidly, for example, when the subject is very active and moves the bedding material around.

▪If you select 0 as Frame weight, the reference image is not updated. This is the same as using Static Subtraction.

▪If you select 100, each sample gets its own reference image with no contribution by the past images. In most cases a Frame weight of 100 does not give good detection, because the subject itself is often removed from the detected image when it moves slowly or sits still.

tip To find the optimal Frame weight, set a value and carry out one or more trials. Evaluate if the tracking was satisfactory. If not, increase or decrease the setting by 20% and try again.