Trackers 2.3.0

Changelog

🚀 Added

• Added OCSORTTracker, a clean re-implementation of OC-SORT. OC-SORT shifts to an observation-centric paradigm, using real detections to correct Kalman filter errors accumulated during occlusions. It introduces Observation-Centric Re-Update (ORU) for state recovery, Observation-Centric Momentum (OCM) for direction-consistency-weighted association, and Observation-Centric Recovery (OCR) for second-stage heuristic matching. OC-SORT achieves the highest HOTA on MOT17 and DanceTrack with default parameters. (#207)

Algorithm	Description	MOT17 HOTA	SportsMOT HOTA	SoccerNet HOTA	DanceTrack HOTA
SORT	Kalman filter + Hungarian matching baseline.	58.4	70.9	81.6	45.0
ByteTrack	Two-stage association using high and low confidence detections.	60.1	73.0	84.0	50.2
OC-SORT	Observation-centric recovery for lost tracks.	61.9	71.7	78.4	51.8

import cv2
import supervision as sv
from inference import get_model
from trackers import OCSORTTracker

model = get_model("rfdetr-medium")
tracker = OCSORTTracker()

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

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

while True:
    ret, frame = cap.read()
    if not ret:
        break

    result = model.infer(frame)[0]
    detections = sv.Detections.from_inference(result)
    detections = tracker.update(detections)

    frame = box_annotator.annotate(frame, detections)
    frame = label_annotator.annotate(frame, detections, labels=detections.tracker_id)

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

cap.release()
cv2.destroyAllWindows()

trackers-2.3.0-promo.mp4

• Added trackers download CLI command and download_dataset Python API. Download benchmark datasets directly from the command line or from code. Supports MOT17 and SportsMOT with split and asset filtering. (#262)

# List available datasets
trackers download --list

# Download full dataset
trackers download mot17

# Download specific split and asset type
trackers download mot17 --split train --annotations-only

# Custom output directory
trackers download sportsmot --split val -o ./datasets

from trackers import download_dataset, Dataset, DatasetSplit, DatasetAsset

download_dataset(
    dataset=Dataset.MOT17,
    split=[DatasetSplit.VAL],
    asset=[DatasetAsset.ANNOTATIONS, DatasetAsset.DETECTIONS],
    output_dir="./data",
)

Dataset	Description	Splits	Assets	License
mot17	Pedestrian tracking with crowded scenes and frequent occlusions.	train, val, test	frames, annotations, detections	CC BY-NC-SA 3.0
sportsmot	Sports broadcast tracking with fast motion and similar-looking targets.	train, val, test	frames, annotations	CC BY 4.0

MOT17_MOT17-02-DPM.mp4

SportsMOT_v_-6Os86HzwCs_c001.mp4

• Added --track-ids flag to trackers track CLI command. Filter displayed tracks by track ID to focus on specific objects in a scene. (#280)

trackers track --source video.mp4 --output output.mp4 \
    --model rfdetr-medium \
    --tracker bytetrack \
    --track-ids 1,2

🌱 Changed

• Made --source optional in trackers track when --detections is provided and no visual output is requested, enabling frameless tracking for evaluation workflows. (#322)

• Optimized xcycsr_to_xyxy and xyxy_to_xcycsr bounding box converters for the single-box hot path, reducing per-call overhead in inner tracking loops. (#296)

🛠️ Fix

• Fixed a bug in MOT evaluation where ground-truth entries with conf=0 (distractors) were not filtered, causing artificially low scores on MOT17. Tracker entries with id < 0 are now also excluded. Results now match TrackEval exactly. (#322)

🏆 Contributors

@JVSCHANDRADITHYA (Chandradithya Janaswami), @salmanmkc (Salman Chishti), @AlexBodner (Alexander Bodner), @Borda (Jirka Borovec), @SkalskiP (Piotr Skalski)