dabble.tracking

Description

🎯 Performs multiple object tracking for detected bboxes.

class Node(config=None, **kwargs)

Uses bounding boxes detected by an object detector model to track multiple objects. dabble.tracking is a useful alternative to model.fairmot and model.jde as it can track bounding boxes detected by the upstream object detector and is not limited to only "person" detections.

Currently, two types of tracking algorithms can be selected: MOSSE and IOU. Information on the algorithms’ performance can be found here.

Inputs

img (numpy.ndarray): A NumPy array of shape \((height, width, channels)\) containing the image data in BGR format.

bboxes (numpy.ndarray): A NumPy array of shape \((N, 4)\) containing normalized bounding box coordinates of \(N\) detected objects. Each bounding box is represented as \((x_1, y_1, x_2, y_2)\) where \((x_1, y_1)\) is the top-left corner and \((x_2, y_2)\) is the bottom-right corner. The order corresponds to bbox_labels and bbox_scores.

Outputs

obj_attrs (Dict[str, Any]): A dictionary of attributes associated with each bounding box, in the same order as bboxes. Different nodes that produce this obj_attrs output type may contribute different attributes. dabble.tracking produces the ids attribute which contains the tracking IDs of the detections.

Configs

• tracking_type (str) – {“iou”, “mosse”}, default=”iou”.
  Type of tracking algorithm to be used. For more information about the trackers, please view the Object Counting (Over Time) use case.

• iou_threshold (float) – [0, 1], default=0.1.
  Minimum IoU value to be used with the matching logic.

• max_lost (int) – [0, sys.maxsize), default=10.
  Maximum number of frames to keep “lost” tracks after which they will be removed. Only used when tracking_type = iou.