tracker.py

import numpy as np
from utils import *

class Sort(object):
  def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
    """
    Sets key parameters for SORT
    """
    self.max_age = max_age
    self.min_hits = min_hits
    self.iou_threshold = iou_threshold
    self.trackers = []
    self.frame_count = 0

  def update(self, dets=np.empty((0, 5))):
    """
    Params:
      dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
    Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
    Returns the a similar array, where the last column is the object ID.
    NOTE: The number of objects returned may differ from the number of detections provided.
    """
    self.frame_count += 1
    # get predicted locations from existing trackers.
    trks = np.zeros((len(self.trackers), 5))
    to_del = []
    ret = []
    for t, trk in enumerate(trks):
      pos = self.trackers[t].predict()[0]
      trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
      if np.any(np.isnan(pos)):
        to_del.append(t)
    trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
    for t in reversed(to_del):
      self.trackers.pop(t)
    matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets,trks, self.iou_threshold)

    # update matched trackers with assigned detections
    for m in matched:
      self.trackers[m[1]].update(dets[m[0], :])

    # create and initialise new trackers for unmatched detections
    for i in unmatched_dets:
        trk = KalmanBoxTracker(dets[i,:])
        self.trackers.append(trk)
    i = len(self.trackers)
    for trk in reversed(self.trackers):
        d = trk.get_state()[0]
        if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
          ret.append(np.concatenate((d,[trk.id+1])).reshape(1,-1)) # +1 as MOT benchmark requires positive
        i -= 1
        # remove dead tracklet
        if(trk.time_since_update > self.max_age):
          self.trackers.pop(i)
    if(len(ret)>0):
      return np.concatenate(ret)
    return np.empty((0,5))