diff --git a/frigate/track/norfair_tracker.py b/frigate/track/norfair_tracker.py
new file mode 100644
index 000000000..a6e94071f
--- /dev/null
+++ b/frigate/track/norfair_tracker.py
@@ -0,0 +1,263 @@
+from collections import defaultdict
+import random
+import string
+
+import numpy as np
+from frigate.config import DetectConfig
+from frigate.track import ObjectTracker
+from frigate.util import intersection_over_union
+from norfair import Detection, Tracker, Drawable, draw_boxes
+from norfair.drawing.drawer import Drawer
+
+
+# Normalizes distance from estimate relative to object size
+# Other ideas:
+# - if estimates are inaccurate for first N detections, compare with last_detection (may be fine)
+# - could be variable based on time since last_detection
+# - include estimated velocity in the distance (car driving by of a parked car)
+# - include some visual similarity factor in the distance for occlusions
+def frigate_distance(detection: Detection, tracked_object) -> float:
+    # calculate distances and normalize it by width and height of previous detection
+    ld = tracked_object.last_detection
+    width = ld.points[1][0] - ld.points[0][0]
+    height = ld.points[1][1] - ld.points[0][1]
+    difference = (detection.points - tracked_object.estimate).astype(float)
+    difference[:, 0] /= width
+    difference[:, 1] /= height
+
+    # calculate euclidean distance and average
+    return np.linalg.norm(difference, axis=1).mean()
+
+
+class NorfairTracker(ObjectTracker):
+    def __init__(self, config: DetectConfig):
+        self.tracked_objects = {}
+        self.disappeared = {}
+        self.positions = {}
+        self.max_disappeared = config.max_disappeared
+        self.detect_config = config
+        self.track_id_map = {}
+        # TODO: could also initialize a tracker per object class if there
+        #       was a good reason to have different distance calculations
+        self.tracker = Tracker(
+            distance_function=frigate_distance,
+            # distance is relative to the size of the last
+            # detection
+            distance_threshold=4.0,
+            initialization_delay=0,
+            hit_counter_max=self.max_disappeared,
+        )
+
+    def register(self, track_id, obj):
+        rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
+        id = f"{obj['frame_time']}-{rand_id}"
+        self.track_id_map[track_id] = id
+        obj["id"] = id
+        obj["start_time"] = obj["frame_time"]
+        obj["motionless_count"] = 0
+        obj["position_changes"] = 0
+        self.tracked_objects[id] = obj
+        self.disappeared[id] = 0
+        self.positions[id] = {
+            "xmins": [],
+            "ymins": [],
+            "xmaxs": [],
+            "ymaxs": [],
+            "xmin": 0,
+            "ymin": 0,
+            "xmax": self.detect_config.width,
+            "ymax": self.detect_config.height,
+        }
+
+    def deregister(self, id):
+        del self.tracked_objects[id]
+        del self.disappeared[id]
+
+    # tracks the current position of the object based on the last N bounding boxes
+    # returns False if the object has moved outside its previous position
+    def update_position(self, id, box):
+        position = self.positions[id]
+        position_box = (
+            position["xmin"],
+            position["ymin"],
+            position["xmax"],
+            position["ymax"],
+        )
+
+        xmin, ymin, xmax, ymax = box
+
+        iou = intersection_over_union(position_box, box)
+
+        # if the iou drops below the threshold
+        # assume the object has moved to a new position and reset the computed box
+        if iou < 0.6:
+            self.positions[id] = {
+                "xmins": [xmin],
+                "ymins": [ymin],
+                "xmaxs": [xmax],
+                "ymaxs": [ymax],
+                "xmin": xmin,
+                "ymin": ymin,
+                "xmax": xmax,
+                "ymax": ymax,
+            }
+            return False
+
+        # if there are less than 10 entries for the position, add the bounding box
+        # and recompute the position box
+        if len(position["xmins"]) < 10:
+            position["xmins"].append(xmin)
+            position["ymins"].append(ymin)
+            position["xmaxs"].append(xmax)
+            position["ymaxs"].append(ymax)
+            # by using percentiles here, we hopefully remove outliers
+            position["xmin"] = np.percentile(position["xmins"], 15)
+            position["ymin"] = np.percentile(position["ymins"], 15)
+            position["xmax"] = np.percentile(position["xmaxs"], 85)
+            position["ymax"] = np.percentile(position["ymaxs"], 85)
+
+        return True
+
+    def is_expired(self, id):
+        obj = self.tracked_objects[id]
+        # get the max frames for this label type or the default
+        max_frames = self.detect_config.stationary.max_frames.objects.get(
+            obj["label"], self.detect_config.stationary.max_frames.default
+        )
+
+        # if there is no max_frames for this label type, continue
+        if max_frames is None:
+            return False
+
+        # if the object has exceeded the max_frames setting, deregister
+        if (
+            obj["motionless_count"] - self.detect_config.stationary.threshold
+            > max_frames
+        ):
+            return True
+
+        return False
+
+    def update(self, track_id, obj):
+        id = self.track_id_map[track_id]
+        self.disappeared[id] = 0
+        # update the motionless count if the object has not moved to a new position
+        if self.update_position(id, obj["box"]):
+            self.tracked_objects[id]["motionless_count"] += 1
+            if self.is_expired(id):
+                self.deregister(id)
+                return
+        else:
+            # register the first position change and then only increment if
+            # the object was previously stationary
+            if (
+                self.tracked_objects[id]["position_changes"] == 0
+                or self.tracked_objects[id]["motionless_count"]
+                >= self.detect_config.stationary.threshold
+            ):
+                self.tracked_objects[id]["position_changes"] += 1
+            self.tracked_objects[id]["motionless_count"] = 0
+
+        self.tracked_objects[id].update(obj)
+
+    def update_frame_times(self, frame_time):
+        # if the object was there in the last frame, assume it's still there
+        detections = [
+            (
+                obj["label"],
+                obj["score"],
+                obj["box"],
+                obj["area"],
+                obj["ratio"],
+                obj["region"],
+            )
+            for id, obj in self.tracked_objects.items()
+            if self.disappeared[id] == 0
+        ]
+        self.match_and_update(frame_time, detections=detections)
+
+    def match_and_update(self, frame_time, detections):
+        norfair_detections = []
+
+        for obj in detections:
+            # centroid is used for other things downstream
+            centroid_x = int((obj[2][0] + obj[2][2]) / 2.0)
+            centroid_y = int((obj[2][1] + obj[2][3]) / 2.0)
+
+            # track based on top,left and bottom,right corners instead of centroid
+            points = np.array([[obj[2][0], obj[2][1]], [obj[2][2], obj[2][3]]])
+
+            norfair_detections.append(
+                Detection(
+                    points=points,
+                    label=obj[0],
+                    data={
+                        "label": obj[0],
+                        "score": obj[1],
+                        "box": obj[2],
+                        "area": obj[3],
+                        "ratio": obj[4],
+                        "region": obj[5],
+                        "frame_time": frame_time,
+                        "centroid": (centroid_x, centroid_y),
+                    },
+                )
+            )
+
+        tracked_objects = self.tracker.update(detections=norfair_detections)
+
+        # update or create new tracks
+        active_ids = []
+        for t in tracked_objects:
+            active_ids.append(t.global_id)
+            if not t.global_id in self.track_id_map:
+                self.register(t.global_id, t.last_detection.data)
+            # if there wasn't a detection in this frame, increment disappeared
+            elif t.last_detection.data["frame_time"] != frame_time:
+                id = self.track_id_map[t.global_id]
+                self.disappeared[id] += 1
+            # else update it
+            else:
+                self.update(t.global_id, t.last_detection.data)
+
+        # clear expired tracks
+        expired_ids = [k for k in self.track_id_map.keys() if k not in active_ids]
+        for e_id in expired_ids:
+            self.deregister(self.track_id_map[e_id])
+            del self.track_id_map[e_id]
+
+    def debug_draw(self, frame, frame_time):
+        active_detections = [
+            Drawable(id=obj.id, points=obj.last_detection.points, label=obj.label)
+            for obj in self.tracker.tracked_objects
+            if obj.last_detection.data["frame_time"] == frame_time
+        ]
+        missing_detections = [
+            Drawable(id=obj.id, points=obj.last_detection.points, label=obj.label)
+            for obj in self.tracker.tracked_objects
+            if obj.last_detection.data["frame_time"] != frame_time
+        ]
+        # draw the estimated bounding box
+        draw_boxes(frame, self.tracker.tracked_objects, color="green", draw_ids=True)
+        # draw the detections that were detected in the current frame
+        draw_boxes(frame, active_detections, color="blue", draw_ids=True)
+        # draw the detections that are missing in the current frame
+        draw_boxes(frame, missing_detections, color="red", draw_ids=True)
+
+        # draw the distance calculation for the last detection
+        # estimate vs detection
+        for obj in self.tracker.tracked_objects:
+            ld = obj.last_detection
+            # bottom right
+            text_anchor = (
+                ld.points[1, 0],
+                ld.points[1, 1],
+            )
+            frame = Drawer.text(
+                frame,
+                f"{obj.id}: {str(obj.last_distance)}",
+                position=text_anchor,
+                size=None,
+                color=(255, 0, 0),
+                thickness=None,
+            )
diff --git a/frigate/video.py b/frigate/video.py
index 78622682a..677b4a177 100755
--- a/frigate/video.py
+++ b/frigate/video.py
@@ -21,6 +21,7 @@ from frigate.motion import MotionDetector
 from frigate.object_detection import RemoteObjectDetector
 from frigate.track import ObjectTracker
 from frigate.track.centroid_tracker import CentroidTracker
+from frigate.track.norfair_tracker import NorfairTracker
 from frigate.track.sort_tracker import SortTracker
 from frigate.util import (
     EventsPerSecond,
@@ -474,7 +475,7 @@ def track_camera(
         name, labelmap, detection_queue, result_connection, model_config, stop_event
     )
 
-    object_tracker = SortTracker(config.detect)
+    object_tracker = NorfairTracker(config.detect)
 
     frame_manager = SharedMemoryFrameManager()
 
@@ -849,6 +850,17 @@ def process_frames(
             else:
                 object_tracker.update_frame_times(frame_time)
 
+        # debug tracking by writing frames
+        if False:
+            bgr_frame = cv2.cvtColor(
+                frame,
+                cv2.COLOR_YUV2BGR_I420,
+            )
+            object_tracker.debug_draw(bgr_frame, frame_time)
+            cv2.imwrite(
+                f"debug/frames/track-{'{:.6f}'.format(frame_time)}.jpg", bgr_frame
+            )
+
         # add to the queue if not full
         if detected_objects_queue.full():
             frame_manager.delete(f"{camera_name}{frame_time}")
diff --git a/requirements-wheels.txt b/requirements-wheels.txt
index 2acee250e..44a40f550 100644
--- a/requirements-wheels.txt
+++ b/requirements-wheels.txt
@@ -20,6 +20,7 @@ requests == 2.30.*
 types-requests == 2.28.*
 scipy == 1.10.*
 similari-trackers-rs == 0.26.*
+norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 # Openvino Library - Custom built with MYRIAD support