Object tracking is the process of identifying objects and features and measuring changes in these objects over time. It is different from looking at a simple before/after, 2-timepoint dataset and measuring macro changes. Instead, tracking focuses on dynamic changes of specific objects through a series of consecutive time points to better understand dynamic processes affecting the changes that can be seen from start to finish.

Tracking systems take a series of images taken over multiple time points, and then try to identify the same specific objects or features from one time-point to the next so that we can measure and monitor specific changes.

Examples of tracking applications include:

  • Calcium ratio experiments - We monitor fluorescence intensity changes between cells
  • Contact analysis - We track specific cells and try to identify the frequency and duration of contact events with other structures in the image, like blood vessels or other cell types
  • Lineage tracking - Monitor where embryonic cells are, how often they divide and where the children of these divisions go
  • Wound healing - Measure the speed at which cells migrate to close a gap between to separate cell tissues

And many others besides. But what all these applications have in common is that we are monitoring how a specific feature (wound area, velocity, intensity, etc) changes over a time series. 

The process of track creation can generally be considered as two separate processes:

  • Identifying and marking specific objects or features
  • Correlating these objects over the course of the time series to create a special group we call a track.

Both of these tasks can be carried out in a variety of ways depending on the specific application.

For example, object recognition can be done through automatic segmentation of an image, or it could be done by creating a simple region of interest and duplicating that region over all the available time points. Tracking can be done manually by a user making interpretations of the image data, or algorithmically by identifying segmented objects from one time-point to the next.

With this in mind, the tracking accuracy is highly dependent both on the ability to recognize the same object accurately from one time-point to the next, and the ability to recognize the objects in each time point individually in the first place. Both of these are highly dependent on the quality of the image data and the sampling frequency.