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<li class="toctree-l1"><a class="reference internal" href="../../includeme/readmefile.html">Multi-object trackers in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/readmefile.html#example-tf-mobilenetssd-centroidtracker">Example: <cite>TF-MobileNetSSD + CentroidTracker</cite></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/readmefile.html#example-yolov3-centroidtracker">Example: <cite>YOLOv3 + CentroidTracker</cite></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html">Tracker</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#sort">SORT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#iou-tracker">IOU Tracker</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#kalman-filter-based-centroid-tracker">Kalman Filter based Centroid Tracker</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../includeme/apidocuments.html#tracks">Tracks</a></li>
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<h1>Source code for motrackers.sort_tracker</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">linear_sum_assignment</span>
<span class="kn">from</span> <span class="nn">motrackers.utils.misc</span> <span class="kn">import</span> <span class="n">iou_xywh</span> <span class="k">as</span> <span class="n">iou</span>
<span class="kn">from</span> <span class="nn">motrackers.track</span> <span class="kn">import</span> <span class="n">KFTrackSORT</span><span class="p">,</span> <span class="n">KFTrack4DSORT</span>
<span class="kn">from</span> <span class="nn">motrackers.centroid_kf_tracker</span> <span class="kn">import</span> <span class="n">CentroidKF_Tracker</span>
<div class="viewcode-block" id="assign_tracks2detection_iou"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.sort_tracker.assign_tracks2detection_iou">[docs]</a><span class="k">def</span> <span class="nf">assign_tracks2detection_iou</span><span class="p">(</span><span class="n">bbox_tracks</span><span class="p">,</span> <span class="n">bbox_detections</span><span class="p">,</span> <span class="n">iou_threshold</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Assigns detected bounding boxes to tracked bounding boxes using IoU as a distance metric.</span>
<span class="sd"> Args:</span>
<span class="sd"> bbox_tracks (numpy.ndarray): Bounding boxes of shape `(N, 4)` where `N` is number of objects already being tracked.</span>
<span class="sd"> bbox_detections (numpy.ndarray): Bounding boxes of shape `(M, 4)` where `M` is number of objects that are newly detected.</span>
<span class="sd"> iou_threshold (float): IOU threashold.</span>
<span class="sd"> Returns:</span>
<span class="sd"> tuple: Tuple contains the following elements in the given order:</span>
<span class="sd"> - matches (numpy.ndarray): Array of shape `(n, 2)` where `n` is number of pairs formed after matching tracks to detections. This is an array of tuples with each element as matched pair of indices`(track_index, detection_index)`.</span>
<span class="sd"> - unmatched_detections (numpy.ndarray): Array of shape `(m,)` where `m` is number of unmatched detections.</span>
<span class="sd"> - unmatched_tracks (numpy.ndarray): Array of shape `(k,)` where `k` is the number of unmatched tracks.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="p">(</span><span class="n">bbox_tracks</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">bbox_detections</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bbox_detections</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bbox_tracks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">bbox_tracks</span> <span class="o">=</span> <span class="n">bbox_tracks</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bbox_detections</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">bbox_detections</span> <span class="o">=</span> <span class="n">bbox_detections</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">iou_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">bbox_tracks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">bbox_detections</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">bbox_tracks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">bbox_detections</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">iou_matrix</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">d</span><span class="p">]</span> <span class="o">=</span> <span class="n">iou</span><span class="p">(</span><span class="n">bbox_tracks</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="p">:],</span> <span class="n">bbox_detections</span><span class="p">[</span><span class="n">d</span><span class="p">,</span> <span class="p">:])</span>
<span class="n">assigned_tracks</span><span class="p">,</span> <span class="n">assigned_detections</span> <span class="o">=</span> <span class="n">linear_sum_assignment</span><span class="p">(</span><span class="o">-</span><span class="n">iou_matrix</span><span class="p">)</span>
<span class="n">unmatched_detections</span><span class="p">,</span> <span class="n">unmatched_tracks</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">bbox_detections</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="k">if</span> <span class="n">d</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">assigned_detections</span><span class="p">:</span>
<span class="n">unmatched_detections</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">bbox_tracks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">assigned_tracks</span><span class="p">:</span>
<span class="n">unmatched_tracks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="c1"># filter out matched with low IOU</span>
<span class="n">matches</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">assigned_tracks</span><span class="p">,</span> <span class="n">assigned_detections</span><span class="p">):</span>
<span class="k">if</span> <span class="n">iou_matrix</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="n">d</span><span class="p">]</span> <span class="o"><</span> <span class="n">iou_threshold</span><span class="p">:</span>
<span class="n">unmatched_detections</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
<span class="n">unmatched_tracks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">matches</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">t</span><span class="p">,</span> <span class="n">d</span><span class="p">))</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">matches</span><span class="p">):</span>
<span class="n">matches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">matches</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">matches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
<span class="k">return</span> <span class="n">matches</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">unmatched_detections</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">unmatched_tracks</span><span class="p">)</span></div>
<div class="viewcode-block" id="SORT"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.sort_tracker.SORT">[docs]</a><span class="k">class</span> <span class="nc">SORT</span><span class="p">(</span><span class="n">CentroidKF_Tracker</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> SORT - Multi object tracker.</span>
<span class="sd"> Args:</span>
<span class="sd"> max_lost (int): Max. number of times a object is lost while tracking.</span>
<span class="sd"> tracker_output_format (str): Output format of the tracker.</span>
<span class="sd"> iou_threshold (float): Intersection over union minimum value.</span>
<span class="sd"> process_noise_scale (float or numpy.ndarray): Process noise covariance matrix of shape (3, 3)</span>
<span class="sd"> or covariance magnitude as scalar value.</span>
<span class="sd"> measurement_noise_scale (float or numpy.ndarray): Measurement noise covariance matrix of shape (1,)</span>
<span class="sd"> or covariance magnitude as scalar value.</span>
<span class="sd"> time_step (int or float): Time step for Kalman Filter.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">max_lost</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">tracker_output_format</span><span class="o">=</span><span class="s1">'mot_challenge'</span><span class="p">,</span>
<span class="n">iou_threshold</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
<span class="n">process_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">measurement_noise_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">time_step</span><span class="o">=</span><span class="mi">1</span>
<span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">iou_threshold</span> <span class="o">=</span> <span class="n">iou_threshold</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">max_lost</span><span class="o">=</span><span class="n">max_lost</span><span class="p">,</span> <span class="n">tracker_output_format</span><span class="o">=</span><span class="n">tracker_output_format</span><span class="p">,</span>
<span class="n">process_noise_scale</span><span class="o">=</span><span class="n">process_noise_scale</span><span class="p">,</span>
<span class="n">measurement_noise_scale</span><span class="o">=</span><span class="n">measurement_noise_scale</span><span class="p">,</span> <span class="n">time_step</span><span class="o">=</span><span class="n">time_step</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_add_track</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="c1"># self.tracks[self.next_track_id] = KFTrackSORT(</span>
<span class="c1"># self.next_track_id, frame_id, bbox, detection_confidence, class_id=class_id,</span>
<span class="c1"># data_output_format=self.tracker_output_format, process_noise_scale=self.process_noise_scale,</span>
<span class="c1"># measurement_noise_scale=self.measurement_noise_scale, **kwargs</span>
<span class="c1"># )</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">next_track_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">KFTrack4DSORT</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">next_track_id</span><span class="p">,</span> <span class="n">frame_id</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">class_id</span><span class="p">,</span>
<span class="n">data_output_format</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tracker_output_format</span><span class="p">,</span> <span class="n">process_noise_scale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">process_noise_scale</span><span class="p">,</span>
<span class="n">measurement_noise_scale</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">measurement_noise_scale</span><span class="p">,</span> <span class="n">kf_time_step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">next_track_id</span> <span class="o">+=</span> <span class="mi">1</span>
<div class="viewcode-block" id="SORT.update"><a class="viewcode-back" href="../../includeme/apidocuments.html#motrackers.sort_tracker.SORT.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bboxes</span><span class="p">,</span> <span class="n">detection_scores</span><span class="p">,</span> <span class="n">class_ids</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">bbox_detections</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">bboxes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'int'</span><span class="p">)</span>
<span class="c1"># track_ids_all = list(self.tracks.keys())</span>
<span class="c1"># bbox_tracks = []</span>
<span class="c1"># track_ids = []</span>
<span class="c1"># for track_id in track_ids_all:</span>
<span class="c1"># bb = self.tracks[track_id].predict()</span>
<span class="c1"># if np.any(np.isnan(bb)):</span>
<span class="c1"># self._remove_track(track_id)</span>
<span class="c1"># else:</span>
<span class="c1"># track_ids.append(track_id)</span>
<span class="c1"># bbox_tracks.append(bb)</span>
<span class="n">track_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">bbox_tracks</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">track_id</span> <span class="ow">in</span> <span class="n">track_ids</span><span class="p">:</span>
<span class="n">bb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">()</span>
<span class="n">bbox_tracks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bb</span><span class="p">)</span>
<span class="n">bbox_tracks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">bbox_tracks</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">bboxes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bbox_tracks</span><span class="p">)):</span>
<span class="n">track_id</span> <span class="o">=</span> <span class="n">track_ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">bbox</span> <span class="o">=</span> <span class="n">bbox_tracks</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">confidence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">detection_confidence</span>
<span class="n">cid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">class_id</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="o">=</span><span class="n">confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">cid</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">lost</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lost</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_remove_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">matches</span><span class="p">,</span> <span class="n">unmatched_detections</span><span class="p">,</span> <span class="n">unmatched_tracks</span> <span class="o">=</span> <span class="n">assign_tracks2detection_iou</span><span class="p">(</span>
<span class="n">bbox_tracks</span><span class="p">,</span> <span class="n">bbox_detections</span><span class="p">,</span> <span class="n">iou_threshold</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">matches</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
<span class="n">t</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">matches</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">track_id</span> <span class="o">=</span> <span class="n">track_ids</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
<span class="n">bbox</span> <span class="o">=</span> <span class="n">bboxes</span><span class="p">[</span><span class="n">d</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">cid</span> <span class="o">=</span> <span class="n">class_ids</span><span class="p">[</span><span class="n">d</span><span class="p">]</span>
<span class="n">confidence</span> <span class="o">=</span> <span class="n">detection_scores</span><span class="p">[</span><span class="n">d</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">confidence</span><span class="p">,</span> <span class="n">cid</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">unmatched_detections</span><span class="p">:</span>
<span class="n">bbox</span> <span class="o">=</span> <span class="n">bboxes</span><span class="p">[</span><span class="n">d</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">cid</span> <span class="o">=</span> <span class="n">class_ids</span><span class="p">[</span><span class="n">d</span><span class="p">]</span>
<span class="n">confidence</span> <span class="o">=</span> <span class="n">detection_scores</span><span class="p">[</span><span class="n">d</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_add_track</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">confidence</span><span class="p">,</span> <span class="n">cid</span><span class="p">)</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">unmatched_tracks</span><span class="p">:</span>
<span class="n">track_id</span> <span class="o">=</span> <span class="n">track_ids</span><span class="p">[</span><span class="n">t</span><span class="p">]</span>
<span class="n">bbox</span> <span class="o">=</span> <span class="n">bbox_tracks</span><span class="p">[</span><span class="n">t</span><span class="p">,</span> <span class="p">:]</span>
<span class="n">confidence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">detection_confidence</span>
<span class="n">cid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">class_id</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frame_count</span><span class="p">,</span> <span class="n">bbox</span><span class="p">,</span> <span class="n">detection_confidence</span><span class="o">=</span><span class="n">confidence</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="n">cid</span><span class="p">,</span> <span class="n">lost</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">[</span><span class="n">track_id</span><span class="p">]</span><span class="o">.</span><span class="n">lost</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lost</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_remove_track</span><span class="p">(</span><span class="n">track_id</span><span class="p">)</span>
<span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_tracks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tracks</span><span class="p">)</span>
<span class="k">return</span> <span class="n">outputs</span></div></div>
</pre></div>
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