class_docstr: DeepSORT tracker
  
  Args:
      input_size (list): input feature map size to reid model, [h, w] format,
          [64, 192] as default.
      min_box_area (int): min box area to filter out low quality boxes
      vertical_ratio (float): w/h, the vertical ratio of the bbox to filter
          bad results, set 1.6 default for pedestrian tracking. If set <=0
          means no need to filter bboxes.
      budget (int): If not None, fix samples per class to at most this number.
          Removes the oldest samples when the budget is reached.
      max_age (int): maximum number of missed misses before a track is deleted
      n_init (float): Number of frames that a track remains in initialization
          phase. Number of consecutive detections before the track is confirmed. 
          The track state is set to `Deleted` if a miss occurs within the first 
          `n_init` frames.
      metric_type (str): either "euclidean" or "cosine", the distance metric 
          used for measurement to track association.
      matching_threshold (float): samples with larger distance are 
          considered an invalid match.
      max_iou_distance (float): max iou distance threshold
      motion (object): KalmanFilter instance
class_docstr_tok: ["DeepSORT","tracker","Args",":","input_size","(","list",")",":","input","feature","map","size","to","reid","model",",","[","h",",","w","]","format",",","[","64",",","192","]","as","default",".","min_box_area","(","int",")",":","min","box","area","to","filter","out","low","quality","boxes","vertical_ratio","(","float",")",":","w\/h",",","the","vertical","ratio","of","the","bbox","to","filter","bad","results",",","set","1.6","default","for","pedestrian","tracking",".","If","set","<","=0","means","no","need","to","filter","bboxes",".","budget","(","int",")",":","If","not","None",",","fix","samples","per","class","to","at","most","this","number",".","Removes","the","oldest","samples","when","the","budget","is","reached",".","max_age","(","int",")",":","maximum","number","of","missed","misses","before","a","track","is","deleted","n_init","(","float",")",":","Number","of","frames","that","a","track","remains","in","initialization","phase",".","Number","of","consecutive","detections","before","the","track","is","confirmed",".","The","track","state","is","set","to","`","Deleted","`","if","a","miss","occurs","within","the","first","`","n_init","`","frames",".","metric_type","(","str",")",":","either","``","euclidean","''","or","``","cosine","''",",","the","distance","metric","used","for","measurement","to","track","association",".","matching_threshold","(","float",")",":","samples","with","larger","distance","are","considered","an","invalid","match",".","max_iou_distance","(","float",")",":","max","iou","distance","threshold","motion","(","object",")",":","KalmanFilter","instance"]
code: def __init__(
      self,
      input_size=[64, 192],
      min_box_area=0,
      vertical_ratio=-1,
      budget=100,
      max_age=70,
      n_init=3,
      metric_type="cosine",
      matching_threshold=0.2,
      max_iou_distance=0.9,
      motion="KalmanFilter",
  ):
      self.input_size = input_size
      self.min_box_area = min_box_area
      self.vertical_ratio = vertical_ratio
      self.max_age = max_age
      self.n_init = n_init
      self.metric = NearestNeighborDistanceMetric(
          metric_type, matching_threshold, budget
      )
      self.max_iou_distance = max_iou_distance
      if motion == "KalmanFilter":
          self.motion = KalmanFilter()
  
      self.tracks = []
      self._next_id = 1
  
file_path: PaddleDetection/deploy/pptracking/python/mot/tracker/deepsort_tracker.py
imports: import numpy
  from ..motion import KalmanFilter
  from ..matching.deepsort_matching import NearestNeighborDistanceMetric
  from ..matching.deepsort_matching import iou_cost, min_cost_matching, matching_cascade, gate_cost_matrix
  from .base_sde_tracker import Track
  from ..utils import Detection
label_desc: Use image node_id 1 to create a new DeepSORTTracker object from inherited base classes: object with example: obj = DeepSORTTracker(input_size, min_box_area, vertical_ratio, budget, max_age, n_init, metric_type, matching_threshold, max_iou_distance, motion)