Functions | |
| def | gate_cost_matrix |
| def | matching_cascade |
| def | min_cost_matching |
Variables | |
| int | INFTY_COST = 1 |
| def deep_sort.linear_assignment.gate_cost_matrix | ( | kf, | |
| cost_matrix, | |||
| tracks, | |||
| detections, | |||
| track_indices, | |||
| detection_indices, | |||
gated_cost = INFTY_COST, |
|||
only_position = False |
|||
| ) |
Invalidate infeasible entries in cost matrix based on the state
distributions obtained by Kalman filtering.
Parameters
----------
kf : The Kalman filter.
cost_matrix : ndarray
The NxM dimensional cost matrix, where N is the number of track indices
and M is the number of detection indices, such that entry (i, j) is the
association cost between `tracks[track_indices[i]]` and
`detections[detection_indices[j]]`.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : List[int]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above).
detection_indices : List[int]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above).
gated_cost : Optional[float]
Entries in the cost matrix corresponding to infeasible associations are
set this value. Defaults to a very large value.
only_position : Optional[bool]
If True, only the x, y position of the state distribution is considered
during gating. Defaults to False.
Returns
-------
ndarray
Returns the modified cost matrix.
Definition at line 144 of file linear_assignment.py.
| def deep_sort.linear_assignment.matching_cascade | ( | distance_metric, | |
| max_distance, | |||
| cascade_depth, | |||
| tracks, | |||
| detections, | |||
track_indices = None, |
|||
detection_indices = None |
|||
| ) |
Run matching cascade.
Parameters
----------
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as well as
a list of N track indices and M detection indices. The metric should
return the NxM dimensional cost matrix, where element (i, j) is the
association cost between the i-th track in the given track indices and
the j-th detection in the given detection indices.
max_distance : float
Gating threshold. Associations with cost larger than this value are
disregarded.
cascade_depth: int
The cascade depth, should be se to the maximum track age.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : Optional[List[int]]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above). Defaults to all tracks.
detection_indices : Optional[List[int]]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above). Defaults to all
detections.
Returns
-------
(List[(int, int)], List[int], List[int])
Returns a tuple with the following three entries:
* A list of matched track and detection indices.
* A list of unmatched track indices.
* A list of unmatched detection indices.
Definition at line 78 of file linear_assignment.py.
| def deep_sort.linear_assignment.min_cost_matching | ( | distance_metric, | |
| max_distance, | |||
| tracks, | |||
| detections, | |||
track_indices = None, |
|||
detection_indices = None |
|||
| ) |
Solve linear assignment problem.
Parameters
----------
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
The distance metric is given a list of tracks and detections as well as
a list of N track indices and M detection indices. The metric should
return the NxM dimensional cost matrix, where element (i, j) is the
association cost between the i-th track in the given track indices and
the j-th detection in the given detection_indices.
max_distance : float
Gating threshold. Associations with cost larger than this value are
disregarded.
tracks : List[track.Track]
A list of predicted tracks at the current time step.
detections : List[detection.Detection]
A list of detections at the current time step.
track_indices : List[int]
List of track indices that maps rows in `cost_matrix` to tracks in
`tracks` (see description above).
detection_indices : List[int]
List of detection indices that maps columns in `cost_matrix` to
detections in `detections` (see description above).
Returns
-------
(List[(int, int)], List[int], List[int])
Returns a tuple with the following three entries:
* A list of matched track and detection indices.
* A list of unmatched track indices.
* A list of unmatched detection indices.
Definition at line 11 of file linear_assignment.py.
Definition at line 8 of file linear_assignment.py.