Returns True if this track is confirmed.

Perform Kalman filter measurement update step and update the feature cache. Parameters ---------- kf : kalman_filter.

Args: frame_id (int): Returns: bool: true if frame_id is recognized

Get current position in bounding box format `(top left x, top left y, width, height)`. Returns ------- ndarray

Convert bounding box to format `(center x, center y, aspect ratio, height)`, where the aspect ratio is `width / height`.

Args: frame_id: bbox_id: Returns: bool: if bbox exists in frame bboxes list

Simple function that adds fixed color depending on the class

Args: output_name: Returns: None Notes: It creates the json output with `output_name` n

Performs Non-Maximum Suppression (NMS) on inference results Returns: detections with shape: nx6 (x1, y1, x2, y2, conf, cls)

Project state distribution to measurement space. Parameters ---------- mean : ndarray The state's mean vector (8

Compute pair-wise cosine distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

Compute pair-wise squared distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

TODO: 1. to float with scale from 0 to 1 2. resize to (64, 128) as Market1501 dataset did 3. concatenate

TODO: Convert bbox from xtl_ytl_w_h to xc_yc_w_h Thanks JieChen91@github.com for reporting this bug!

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Arguments:

Compute the average precision, given the recall and precision curves. Source: https://github.com/rbgirshick/py-faster-rcnn. # Arguments

Compute distance between features and targets. Parameters ---------- features : ndarray An NxM matrix of N featur

Args: frame_id: bbox_id: Returns: bbox_id (int): Raises: ValueError: if bb

Compute gating distance between state distribution and measurements. A suitable distance threshold can be obtained from `chi2inv95`. If

Create track from unassociated measurement. Parameters ---------- measurement : ndarray Bounding box coordinates

Computer intersection over union. Parameters ---------- bbox : ndarray A bounding box in format `(top left x, top left y, width,

Returns True if this track is dead and should be deleted.

Creates kmeans-evolved anchors from training dataset Arguments: path: path to dataset *.yaml, or a loaded dataset n:

Mark this track as missed (no association at the current time step).

Solve linear assignment problem. Parameters ---------- distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) ->

Suppress overlapping detections. Original code from [1]_ has been adapted to include confidence score. .. [1] http://www.pyimagesearch.com/2

Update the distance metric with new data. Parameters ---------- features : ndarray An NxM matrix of N features of

Propagate the state distribution to the current time step using a Kalman filter prediction step. Parameters ----------