diff --git a/OTAnalytics/plugin_datastore/track_store.py b/OTAnalytics/plugin_datastore/track_store.py
index fed307871..c96d38bc9 100644
--- a/OTAnalytics/plugin_datastore/track_store.py
+++ b/OTAnalytics/plugin_datastore/track_store.py
@@ -78,7 +78,7 @@ def frame(self) -> int:
 
     @property
     def occurrence(self) -> datetime:
-        return self._occurrence[1]
+        return self._occurrence
 
     @property
     def interpolated_detection(self) -> bool:
@@ -282,7 +282,7 @@ def as_generator(self) -> Generator[Track, None, None]:
             yield from []
         else:
             for current in track_ids.array:
-                yield self.__create_track_flyweight(current)
+                yield self._create_track_flyweight(current)
 
     @staticmethod
     def from_list(
@@ -354,7 +354,7 @@ def get_for(self, id: TrackId) -> Optional[Track]:
         if self._dataset.empty:
             return None
         try:
-            return self.__create_track_flyweight(id.id)
+            return self._create_track_flyweight(id.id)
         except KeyError:
             return None
 
@@ -393,11 +393,15 @@ def as_list(self) -> list[Track]:
             "Creating track flyweight objects which is really slow in "
             f"'{PandasTrackDataset.as_list.__name__}'."
         )
-        return [self.__create_track_flyweight(current) for current in track_ids]
+        return [self._create_track_flyweight(current) for current in track_ids]
 
-    def __create_track_flyweight(self, track_id: str) -> Track:
-        track_frame = self._dataset.loc[[track_id], :]
-        return PandasTrack(track_id, track_frame)
+    def _create_track_flyweight(self, track_id: str) -> Track:
+        track_frame = self._dataset.loc[track_id, :]
+        if isinstance(track_frame, DataFrame):
+            return PandasTrack(track_id, track_frame)
+        if isinstance(track_frame, Series):
+            return PandasTrack(track_id, track_frame.to_frame(track_id))
+        raise NotImplementedError(f"Not implemented for {type(track_frame)}")
 
     def get_data(self) -> DataFrame:
         return self._dataset
@@ -444,10 +448,9 @@ def __len__(self) -> int:
         return len(self._dataset.index.get_level_values(LEVEL_TRACK_ID).unique())
 
     def filter_by_min_detection_length(self, length: int) -> "PandasTrackDataset":
-        # groupby.size always returns a series
-        detection_counts_per_track: Series[int] = self._dataset.groupby(  # type: ignore
+        detection_counts_per_track: Series[int] = self._dataset.groupby(
             level=LEVEL_TRACK_ID
-        ).size()
+        )[track.FRAME].size()
         filtered_ids = detection_counts_per_track[
             detection_counts_per_track >= length
         ].index
diff --git a/tests/OTAnalytics/plugin_datastore/test_track_store.py b/tests/OTAnalytics/plugin_datastore/test_track_store.py
index 3bfadb851..b56d8dc9d 100644
--- a/tests/OTAnalytics/plugin_datastore/test_track_store.py
+++ b/tests/OTAnalytics/plugin_datastore/test_track_store.py
@@ -60,7 +60,7 @@ def test_properties(self) -> None:
         python_detection = builder.build_detections()[0]
         data = Series(
             python_detection.to_dict(),
-            name=(python_detection.track_id.id, python_detection.occurrence),
+            name=python_detection.occurrence,
         )
         pandas_detection = PandasDetection(python_detection.track_id.id, data)
 
@@ -77,12 +77,9 @@ def test_properties(self) -> None:
         builder.append_detection()
         python_track = builder.build_track()
         detections = [detection.to_dict() for detection in python_track.detections]
-        data = (
-            DataFrame(detections)
-            .set_index([track.TRACK_ID, track.OCCURRENCE])
-            .sort_index()
-        )
+        data = DataFrame(detections).set_index([track.OCCURRENCE]).sort_index()
         data[track.TRACK_CLASSIFICATION] = data[track.CLASSIFICATION]
+        data = data.drop([track.TRACK_ID], axis=1)
         pandas_track = PandasTrack(python_track.id.id, data)
 
         assert_equal_track_properties(pandas_track, python_track)
@@ -628,3 +625,15 @@ def test_get_max_confidences_for(
 
         result = filled_dataset.get_max_confidences_for([car_id, pedestrian_id])
         assert result == {car_id: 0.8, pedestrian_id: 0.9}
+
+    def test_create_test_flyweight_with_single_detection(
+        self, track_geometry_factory: TRACK_GEOMETRY_FACTORY
+    ) -> None:
+        track_builder = TrackBuilder()
+        track_builder.append_detection()
+        single_detection_track = track_builder.build_track()
+        dataset = PandasTrackDataset.from_list(
+            [single_detection_track], track_geometry_factory
+        )
+        result = dataset._create_track_flyweight(single_detection_track.id.id)
+        assert_equal_track_properties(result, single_detection_track)