Parses the yolo-v3 layer configuration file and returns module definitions

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

Update a matched track :type new_track: STrack :type frame_id: int :type update_feature: bool :return:

Removes detections with lower object confidence score than 'conf_thres' Non-Maximum Suppression to further filter detections. Returns

Returns the IoU of two bounding boxes

Processes the video sequence given and provides the output of tracking result (write the results in video file) It uses JDE model f

Project state distribution to measurement space. Parameters ---------- mean : ndarray The state's mean vecto

Start a new tracklet

Computes the average precision, given the recall and precision curves. Method originally from https://github.com/rafaelpadilla/Object-Detection-

returns nT, nCorrect, tx, ty, tw, th, tconf, tcls

Computes the average precision, given the recall and precision curves. Code originally from https://github.com/rbgirshick/py-faster-rcnn. #

Constructs module list of layer blocks from module configuration in module_defs

:param: delta_map, shape (nB, nA, nGh, nGw, 4) :param: anchors, shape (nA,4)

Vectorized, approximated, fast NMS, adopted from YOLACT: https://github.com/dbolya/yolact/blob/master/layers/functions/detection.py Th

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 coordin

We resize both tensors to [A,B,2] without new malloc: [A,2] -> [A,1,2] -> [A,B,2] [B,2] -> [1,B,2] -> [A,B,2] Then we compute the area

Compute cost based on IoU :type atlbrs: list[tlbr] | np.ndarray :type atlbrs: list[tlbr] | np.ndarray :rtype ious np.ndarray

Compute the jaccard overlap of two sets of boxes. The jaccard overlap is simply the intersection over union of two boxes. Here we operate on

Run Kalman filter correction step. Parameters ---------- mean : ndarray The predicted state's mean vector (8

Processes the image frame and finds bounding box(detections). Associates the detection with corresponding tracklets and also hand

Iterator function for train dataset

:param tracks: list[STrack] :param detections: list[BaseTrack] :param metric: :return: cost_matrix np.ndarray