The Deep Learning Object Tracker is designed for accurate detection and tracking of people, vehicles, and key objects in challenging environments where motion-based tracking methods would struggle.
The list of objects detected by the Deep Learning Object Tracker is given below:
|
Class Name |
Description |
|---|---|
|
|
A person, or tracked object with a person present (e.g bicycle) |
|
|
A motorcycle |
|
|
A bicycle |
|
|
Person riding a bicycle, can be reported as two separate objects |
|
|
A bus |
|
|
A car |
|
|
A van, including mini-vans and mini-buses |
|
|
A truck, including lorries and commercial work vehicles, |
|
|
A forklift truck |
|
|
A backpack or holdall (sports bag) |
|
|
|
The Deep Learning Object Tracker is based on a classification and detection model, providing the location of an object in the field of view. See Deep Learning Requirements for hardware requirements for this algorithm.
The Deep Learning Object Tracker has the following settings:
Stationary Object Filtering
See Stationary Hold On Time
In addition to the Stationary Hold On Time, an additional setting Require Initial Movement, is available, which will prevent objects that have not moved from being tracked.
Detection Point of Tracked Objects
See Detection Point of Tracked Objects.
Tamper Detection (DLOT)
Learn more about Tamper Detection.
Loss Of Signal Emit Interval
See Loss Of Signal Emit Interval