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SORT

Overview

SORT is a classic online, tracking-by-detection method that predicts object motion with a Kalman filter and matches predicted tracks to detections using the Hungarian algorithm based on Intersection over Union (IoU). The tracker uses only geometric cues from bounding boxes, without appearance features, so it runs extremely fast and scales to hundreds of frames per second on typical hardware. Detections from a strong CNN detector feed SORT, which updates each track’s state via a constant velocity motion model and prunes stale tracks. Because SORT lacks explicit re-identification or appearance cues, it can suffer identity switches and fragmented tracks under long occlusions or heavy crowding.

Benchmarks

For comparisons with other trackers, plus full details on the datasets and evaluation metrics used, see the benchmarks page.

Dataset HOTA IDF1 MOTA
MOT17 58.4 69.9 67.2
SportsMOT 70.9 68.9 95.7
SoccerNet 81.6 76.2 95.1

Run on video, webcam, or RTSP stream

These examples use OpenCV for decoding and display. Replace <SOURCE_VIDEO_PATH>, <WEBCAM_INDEX>, and <RTSP_STREAM_URL> with your inputs. <WEBCAM_INDEX> is usually 0 for the default camera.

import cv2
import supervision as sv
from rfdetr import RFDETRMedium
from trackers import SORTTracker

tracker = SORTTracker()
model = RFDETRMedium()

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

video_capture = cv2.VideoCapture("<SOURCE_VIDEO_PATH>")
if not video_capture.isOpened():
    raise RuntimeError("Failed to open video source")

while True:
    success, frame_bgr = video_capture.read()
    if not success:
        break

    frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
    detections = model.predict(frame_rgb)
    detections = tracker.update(detections)

    annotated_frame = box_annotator.annotate(frame_bgr, detections)
    annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id)

    cv2.imshow("RF-DETR + SORT", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

import cv2
import supervision as sv
from rfdetr import RFDETRMedium
from trackers import SORTTracker

tracker = SORTTracker()
model = RFDETRMedium()

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

video_capture = cv2.VideoCapture("<WEBCAM_INDEX>")
if not video_capture.isOpened():
    raise RuntimeError("Failed to open webcam")

while True:
    success, frame_bgr = video_capture.read()
    if not success:
        break

    frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
    detections = model.predict(frame_rgb)
    detections = tracker.update(detections)

    annotated_frame = box_annotator.annotate(frame_bgr, detections)
    annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id)

    cv2.imshow("RF-DETR + SORT", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

import cv2
import supervision as sv
from rfdetr import RFDETRMedium
from trackers import SORTTracker

tracker = SORTTracker()
model = RFDETRMedium()

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

video_capture = cv2.VideoCapture("<RTSP_STREAM_URL>")
if not video_capture.isOpened():
    raise RuntimeError("Failed to open RTSP stream")

while True:
    success, frame_bgr = video_capture.read()
    if not success:
        break

    frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
    detections = model.predict(frame_rgb)
    detections = tracker.update(detections)

    annotated_frame = box_annotator.annotate(frame_bgr, detections)
    annotated_frame = label_annotator.annotate(annotated_frame, detections, labels=detections.tracker_id)

    cv2.imshow("RF-DETR + SORT", annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

API

trackers.core.sort.tracker.SORTTracker

Bases: BaseTracker

Implements SORT (Simple Online and Realtime Tracking).

SORT is a pragmatic approach to multiple object tracking with a focus on simplicity and speed. It uses a Kalman filter for motion prediction and the Hungarian algorithm or simple IOU matching for data association.

Parameters:

Name Type Description Default
lost_track_buffer int

Number of frames to buffer when a track is lost. Increasing lost_track_buffer enhances occlusion handling, significantly improving tracking through occlusions, but may increase the possibility of ID switching for objects with similar appearance.

 30
frame_rate float

Frame rate of the video (frames per second). Used to calculate the maximum time a track can be lost.

 30.0
track_activation_threshold float

Detection confidence threshold for track activation. Only detections with confidence above this threshold will create new tracks. Increasing this threshold reduces false positives but may miss real objects with low confidence.

 0.25
minimum_consecutive_frames int

Number of consecutive frames that an object must be tracked before it is considered a 'valid' track. Increasing minimum_consecutive_frames prevents the creation of accidental tracks from false detection or double detection, but risks missing shorter tracks. Before the tracker is considered valid, it will be assigned -1 as its tracker_id.

 3
minimum_iou_threshold float

IOU threshold for associating detections to existing tracks.

 0.3

update(detections)

Updates the tracker state with new detections.

Performs Kalman filter prediction, associates detections with existing trackers based on IOU, updates matched trackers, and initializes new trackers for unmatched high-confidence detections.

Parameters:

Name Type Description Default
detections Detections

The latest set of object detections from a frame.

 required

Returns:

Type Description
Detections

A copy of the input detections, augmented with assigned tracker_id for each successfully tracked object. Detections not associated with a track will not have a tracker_id.

reset()

Resets the tracker's internal state.

Clears all active tracks and resets the track ID counter.

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