Multi-Object Tracker
Create and manage tracks of multiple objects
- Library:
Automated Driving Toolbox
Description
The Multi-Object Tracker block initializes, confirms, predicts, corrects, and deletes the tracks of moving objects. Inputs to the multi-object tracker are detection reports generated by Radar Detection Generator and Vision Detection Generator blocks. The multi-object tracker accepts detections from multiple sensors and assigns them to tracks using a global nearest neighbor (GNN) criterion. Each detection is assigned to a separate track. If the detection cannot be assigned to any track, the multi-object tracker creates a new track.
A new track starts in a tentative state. If enough detections are assigned to a tentative track, its status changes to confirmed. When a track is confirmed, the multi-object tracker considers that track to represent a physical object. If detections are not added to the track within a specifiable number of updates, the track is deleted.
The multi-object tracker also estimates the state vector and state vector covariance matrix for each track using a Kalman filter. These state vectors are used to predict a track's location in each frame and determine the likelihood of each detection being assigned to each track.
Ports
Input
Detections — Detection list
Simulink® bus containing MATLAB® structure
Detection list, specified as a Simulink bus containing a MATLAB structure. See Group Signal Lines into Virtual Buses (Simulink). The structure has the form:
| Field | Description | Type |
|---|---|---|
NumDetections | Number of detections | integer |
IsValidTime | False when updates are requested at times that are between block invocation intervals | Boolean |
Detections | Object detections | Array of object detection structures. The first
NumDetections of these
detections are actual detections. |
The definitions of the object detection structures are found in the Detections output port descriptions of the Radar Detection Generator and Vision Detection Generator blocks.
Note
The object detection structure contains a
Time field. The time tag of each object
detection must be less than or equal to the time of the current
invocation of the block. The time tag must also be greater than
the update time specified in the previous invocation of the
block.
Prediction Time — Track update time
real scalar
Track update time, specified as a real scalar. The multi-object tracker updates all tracks to this time. Update time must always increase with each invocation of the block. Units are in seconds.
Note
The object detection structure contains a
Time field. The time tag of each object
detection must be less than or equal to the time of the current
invocation of the block. The time tag must also be greater than
the update time in the previous invocation of the block.
Dependencies
To enable this port, set Prediction time
source to Input
port.
Cost Matrix — Cost matrix
real-valued
Nt-by-Nd
matrix
Cost matrix, specified as a real-valued Nt-by-Nd matrix, where Nt is the number of existing tracks and Nd is the number of current detections.
The rows of the cost matrix correspond to the existing tracks. The columns correspond to the detections. Tracks are ordered as they appear in the list of tracks in the All Tracks output port of the previous invocation of the block.
In the first update to the multi-object tracker, or if the track has
no previous tracks, assign the cost matrix a size of [0,
Nd]. The cost must be
calculated so that lower costs indicate a higher likelihood that the
multi-object tracker assigns a detection to a track. To prevent certain
detections from being assigned to certain tracks, use
Inf.
Dependencies
To enable this port, select Enable cost matrix input.
Detectable Track IDs — Detectable track IDs
real-valued M-by-1 vector | real-valued M-by-2 matrix
Detectable track IDs, specified as a real-valued M-by-1 vector or M-by-2 matrix. Detectable tracks are tracks that the sensors expect to detect. The first column of the matrix contains a list of track IDs that the sensors report as detectable. The optional second column contains the detection probability for the track.
Tracks whose identifiers are not included in Detectable Track IDs are considered undetectable. The track deletion logic does not count the lack of detection as a "missed detection" for track deletion purposes.
If this port is not enabled, the tracker assumes all tracks to be detectable at each invocation of the block.
Dependencies
To enable this port, in the Port Setting tab, select Enable detectable track IDs Input.
Output
Confirmed Tracks — Confirmed tracks
Simulink bus containing MATLAB structure
Confirmed tracks, returned as a Simulink bus containing a MATLAB structure. See Create Nonvirtual Buses (Simulink).
This table shows the structure fields.
| Field | Description |
|---|---|
NumTracks | Number of tracks |
Tracks | Array of track structures of a length set by the Maximum number
of tracks parameter. Only the first
NumTracks of these are actual tracks. |
This table shows the fields of each track structure.
| Field | Description |
TrackID | Unique integer that identifies the track. |
SourceIndex | Unique identifier the tracker in a
multiple tracker environment. The
|
UpdateTime | The time the track was updated. |
Age | Number of times the track survived. |
State | Value of state vector at the update time. |
StateCovariance | Uncertainty covariance matrix. |
Extent | Spatial extent estimate of the
tracked object, returned as a
d-by-d
matrix, where d is the
dimension of the object. This field is only
returned when the tracking filter is specified as
a |
MeasurementRate | Expected number of detections from
the tracked object. This field is only returned
when the tracking filter is specified as a |
IsConfirmed | True if the track is assumed to be of a real target. |
IsCoasted |
|
ObjectClassID |
|
StateParameters | Parameters about the track state
reference frame specified in the
|
IsSelfReported | Indicate if the track is reported by
the tracker. This field is used in a track fusion
environment. It is returned as
|
A track is confirmed if:
At least M detections are assigned to the track during the first N updates after track initialization. To specify the values M and N, use the M and N for the M-out-of-N confirmation parameter.
The detection initiating the track has an
ObjectClassIDgreater than zero.
Tentative Tracks — Tentative tracks
Simulink bus containing MATLAB structure
Tentative tracks, returned as a Simulink bus containing a MATLAB structure. See Create Nonvirtual Buses (Simulink). A track is tentative before it is confirmed.
This table shows the structure fields.
| Field | Description |
|---|---|
NumTracks | Number of tracks |
Tracks | Array of track structures of a length set by the Maximum number
of tracks parameter. Only the first
NumTracks of these are actual tracks. |
This table shows the fields of each track structure.
| Field | Description |
TrackID | Unique integer that identifies the track. |
SourceIndex | Unique identifier the tracker in a
multiple tracker environment. The
|
UpdateTime | The time the track was updated. |
Age | Number of times the track survived. |
State | Value of state vector at the update time. |
StateCovariance | Uncertainty covariance matrix. |
Extent | Spatial extent estimate of the
tracked object, returned as a
d-by-d
matrix, where d is the
dimension of the object. This field is only
returned when the tracking filter is specified as
a |
MeasurementRate | Expected number of detections from
the tracked object. This field is only returned
when the tracking filter is specified as a |
IsConfirmed | True if the track is assumed to be of a real target. |
IsCoasted |
|
ObjectClassID |
|
StateParameters | Parameters about the track state
reference frame specified in the
|
IsSelfReported | Indicate if the track is reported by
the tracker. This field is used in a track fusion
environment. It is returned as
|
Dependencies
To enable this port, select Enable tentative tracks output.
All Tracks — All tracks
Simulink bus containing MATLAB structure
Combined list of confirmed and tentative tracks, returned as a Simulink bus containing a MATLAB structure. See Create Nonvirtual Buses (Simulink).
This table shows the structure fields.
| Field | Description |
|---|---|
NumTracks | Number of tracks |
Tracks | Array of track structures of a length set by the Maximum number
of tracks parameter. Only the first
NumTracks of these are actual tracks. |
This table shows the fields of each track structure.
| Field | Description |
TrackID | Unique integer that identifies the track. |
SourceIndex | Unique identifier the tracker in a
multiple tracker environment. The
|
UpdateTime | The time the track was updated. |
Age | Number of times the track survived. |
State | Value of state vector at the update time. |
StateCovariance | Uncertainty covariance matrix. |
Extent | Spatial extent estimate of the
tracked object, returned as a
d-by-d
matrix, where d is the
dimension of the object. This field is only
returned when the tracking filter is specified as
a |
MeasurementRate | Expected number of detections from
the tracked object. This field is only returned
when the tracking filter is specified as a |
IsConfirmed | True if the track is assumed to be of a real target. |
IsCoasted |
|
ObjectClassID |
|
StateParameters | Parameters about the track state
reference frame specified in the
|
IsSelfReported | Indicate if the track is reported by
the tracker. This field is used in a track fusion
environment. It is returned as
|
Dependencies
To enable this port, select Enable all tracks output.
Parameters
Tracker Management
Tracker identifier — Unique tracker identifier
0 (default) | nonnegative integer
Unique tracker identifier, specified as a nonnegative integer. This
parameter is used as the SourceIndex in the outputs,
and distinguishes tracks that come from different trackers in a
multiple-tracker system. You must specify this property as a positive
integer to use the track outputs as inputs to a track fuser.
Example: 1
Filter initialization function name — Kalman filter initialization function
initcvkf (default) | function name
Kalman filter initialization function, specified as a function name.
The toolbox provides several initialization functions. For an example of
an initialization function, see initcvekf.
Threshold for assigning detections to tracks — Detection assignment threshold
30.0 (default) | positive real scalar
Detection assignment threshold, specified as a positive real scalar. To assign a detection to a track, the detection's normalized distance from the track must be less than the assignment threshold. If some detections remain unassigned to tracks that you want them assigned to, then increase the threshold. If some detections are assigned to incorrect tracks, decrease the threshold.
M and N for the M-out-of-N confirmation — Confirmation parameters for track creation
[2,3] (default) | two-element vector of positive integers
Confirmation parameters for track creation, specified as a two-element
vector of positive integers, [M,N]. A track is
confirmed when at least M detections are assigned to
the track during the first N updates after track
initialization. M must be less than or equal to
N.
When setting
N, consider the number of times you want the tracker to update before it confirms a track. For example, if a tracker updates every 0.05 seconds, and you allow 0.5 seconds to make a confirmation decision, setN= 10.When setting
M, take into account the probability of object detection for the sensors. The probability of detection depends on factors such as occlusion or clutter. You can reduceMwhen tracks fail to be confirmed or increaseMwhen too many false detections are assigned to tracks.
Example: [3,5]
P and R for the P-out-of-R deletion — Track deletion threshold
[5 5]
(default) | real-valued 1-by-2 vector of positive integers
Track deletion threshold for history logic, specified as a real-valued
1-by-2 vector of positive integers [P R]. If a
confirmed track is not assigned to any detection P
times in the last Q tracker updates, then the track
is deleted.
Maximum number of tracks — Maximum number of tracks
200 (default) | positive integer
Maximum number of tracks that the block can process, specified as a positive integer.
Maximum number of sensors — Maximum number of sensors
20 (default) | positive integer
Maximum number of sensors that the block can process, specified as a
positive integer. This value should be greater than or equal to the
highest SensorIndex value used in the
Detections input port.
Track state parameters — Parameters of the track state reference frame
struct (default) | struct | struct array
Parameters of the track state reference frame, specified as a struct or a struct array. Use this property to define the track state reference frame and how to transform the track from the tracker (called source) coordinate system to the fuser coordinate system.
Inputs and Outputs
Prediction time source — Source for prediction time
Input port (default) | Auto
Source for prediction time, specified as Input
port or Auto. Select
Input port to input an update time by
using the Prediction Time input port. Otherwise,
the simulation clock managed by Simulink determines the update time.
Example: Auto
Enable cost matrix input — Enable input port for cost matrix
off (default) | on
Select this check box to enable the input of a cost matrix by using the Cost Matrix input port.
Enable detectable track IDs input — Enable detectable track IDs input
off (default) | on
Select this check box to enable the Detectable Track IDs input port.
Source of output bus name — Source of output bus name
Auto (default) | Property
Source of output bus name, specified as Auto or
Property.
If you select
Auto, the block automatically creates a bus name.If you select
Property, specify the bus name using the Specify an output bus name parameter.
Specify an output bus name — Name of output bus
no default
Dependencies
To enable this parameter, set the Source of output bus
name parameter to Property.
Enable tentative tracks output — Enable output port for tentative tracks
off (default) | on
Select this check box to enable the output of tentative tracks by using the Tentative Tracks output port.
Enable all tracks output — Enable output port for all tracks
off (default) | on
Select this check box to enable the output of all the tracks by using the All Tracks output port.
Simulate using — Type of simulation to run
Interpreted execution (default) | Code generation
Interpreted execution— Simulate the model using the MATLAB interpreter. This option shortens startup time. InInterpreted executionmode, you can debug the source code of the block.Code generation— Simulate the model using generated C/C++ code. The first time you run a simulation, Simulink generates C/C++ code for the block. The C code is reused for subsequent simulations as long as the model does not change. This option requires additional startup time.
