Duplicate from Hamidreza-Hashemp/FastTracker-Benchmark

Co-authored-by: Hamidreza Hashemp <Hamidreza-Hashemp@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

GT/task_day_left_turn/gt/labels.txt

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+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_day_occlusion/gt/labels.txt

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+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_day_pedestrian_crossing/gt/labels.txt

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+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_day_right_turn/gt/labels.txt

@@ -0,0 +1,15 @@
+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_day_right_turn_occlusion/gt/labels.txt

@@ -0,0 +1,15 @@
+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_day_right_turn_pedestrian/gt/labels.txt

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+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_night_far_objects/gt/labels.txt

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+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_night_occlusion/gt/labels.txt

@@ -0,0 +1,15 @@
+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_night_occlusion_far_objects/gt/labels.txt

@@ -0,0 +1,15 @@
+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_night_right_turn_pedestrian/gt/labels.txt

@@ -0,0 +1,15 @@
+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_night_right_turn_pedestrian_occlusion/gt/labels.txt

@@ -0,0 +1,15 @@
+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

GT/task_tunnel/gt/labels.txt

@@ -0,0 +1,15 @@
+person
+bus_small
+bus_big
+truck_small
+truck_big
+car
+bike
+motorbike
+ignore_region
+tractor
+trailor
+wheelchair
+heavy_equipment
+pm
+umbrella

README.md

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+---
+license: bigscience-openrail-m
+task_categories:
+- object-detection
+language:
+- en
+tags:
+- Multi-object-tracking
+pretty_name: FastTracker-Benchmark
+size_categories:
+- 100K<n<1M
+---
+
+# FastTracker Benchmark
+
+### A new benchmark dataset comprising diverse vehicle classes with frame-level tracking annotation introduced in paper: *FastTracker: Real-Time and Accurate Visual Tracking*
+_[Hamidreza Hashempoor](https://hamidreza-hashempoor.github.io/),  Yu Dong Hwang_.
+
+## Resources
+| Github  | Paper |
+|:-----------------:|:-------:|
+|[![github](https://img.shields.io/badge/Github-Code-blue)](https://github.com/Hamidreza-Hashempoor/FastTracker)|[![arXiv](https://img.shields.io/badge/arXiv-2508.14370-blue)](https://arxiv.org/abs/2508.14370)
+
+
+<div align="center">
+  <img src="./fig/fasttrack_benchmark.jpg" width="40%" alt="MiroThinker" />
+</div>
+
+
+---
+
+
+
+## Dataset Overview
+
+
+
+Brief statistics and visualization of FastTracker benchmark and its comparison with other benchmarks.
+
+| Attribute      | UrbanTracker | CityFlow | FastTracker |
+|----------------|--------------|----------|-----------|
+| **Year**       | 2014         | 2022     | 2025      |
+| **Detections** | 12.5K        | 890K     | 800K      |
+| **#Videos**    | 5            | 40       | 12        |
+| **Obj/Frame**  | 5.4          | 8.2      | 43.5      |
+| **#Classes**   | 3            | 1        | 9         |
+| **#Scenarios** | 1            | 4        | 12        |
+
+
+
+---
+
+## Dataset Summary
+
+- **What is it?**  
+  FastTrack is a large-scale benchmark dataset for evaluating multi-object tracking in complex and high-density traffic environments. It includes 800K annotated object detections across 12 videos, with an average of 43.5 objects per frame. The dataset features 9 traffic-related classes and covers diverse real-world traffic scenarios—such as multilane intersections, tunnels, crosswalks, and merging roads—captured under varying lighting conditions (daytime, nighttime, shadows).
+
+- **Why was it created?**  
+  FastTrack was created to address limitations of existing benchmarks like UrbanTracker and CityFlow, which lack diversity in scene types and have lower object density. This benchmark introduces challenging conditions including extreme crowding, long-term occlusions, and diverse motion patterns, to push the boundaries of modern multi-object tracking algorithms—particularly those optimized for real-world, urban traffic settings.
+
+- **What can it be used for?**  
+  Multi-object tracking, re-identification, online tracking evaluation, urban scene understanding, and benchmarking tracking algorithms under occlusion and crowding.
+
+- **Who are the intended users?**  
+  Researchers and practitioners in computer vision and intelligent transportation systems, especially those focusing on real-time tracking, urban mobility, autonomous driving, and edge deployment. Also valuable for students and developers working on lightweight or environment-aware tracking models.
+
+---
+
+
+## Dataset Structure
+
+### Data Format
+
+GT format is like (each line):
+`frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, 1.0`.
+
+To prepare the dataset, first run `extract_frames.py` to decode frames from each video.  
+In **line 11** of the script, add the video filename and the number of frames you want to extract.  
+```bash
+python extract_frames.py
+```
+
+Then, convert the ground truth into COCO format with:
+```bash
+python convert_to_coco.py
+```
+This will generate annotations/train.json ready for training your detector.
+
+
+
+## Citation
+If you use our code or Benchmark, please cite our work.
+
+
+```
+@misc{hashempoor2025fasttrackerrealtimeaccuratevisual,
+      title={FastTracker: Real-Time and Accurate Visual Tracking}, 
+      author={Hamidreza Hashempoor and Yu Dong Hwang},
+      year={2025},
+      eprint={2508.14370},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2508.14370}, 
+}
+```

Videos_/night_occlusion_far_objects.mp4

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+size 29183584

Videos_/task_day_left_turn.mp4

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+size 19418590

Videos_/task_day_occlusion.mp4

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+oid sha256:0b2ce1fe23c6615aafa738244cc942464f79d6df87d5600ba4a49692ec74b0db
+size 17578267

Videos_/task_day_pedestrian_crossing.mp4

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+oid sha256:365ffbaf54424f7e303a0fc71ba3dfd97dcd24dadfac08d64d08e41b61ff79ca
+size 16706342

Videos_/task_day_right_turn.mp4

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+oid sha256:024dc305add0da14d7d37c445c089f4f7ea003113c660acd4afca2ed1be18097
+size 17972349

Videos_/task_day_right_turn_occlusion.mp4

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+oid sha256:16414e766e0c3b721cbd573e4955e5a2fc9ca4b596d718dee963428cfb634822
+size 17795404

Videos_/task_day_right_turn_pedestrian.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11949b92474ffd1b27b33617ca8f53a952c801cd00f4e0bec1fa9e12d5d5f1fe
+size 17085242

Videos_/task_night_far_objects.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c46a6698a0a86185317c236ac61aeb838a6223d98f4e66df0037576135470171
+size 29490160

Videos_/task_night_occlusion.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:627c0dc4ea2145538b19d1610c555eddc48740863c0155e998c3c1553ddebbba
+size 15835611

Videos_/task_night_right_turn_pedestrian.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:87ef2a06a722c2bebc0c5b7dd08b8072c9b22873f9e9d1dafd1836cb52a685f8
+size 16784463

Videos_/task_night_right_turn_pedestrian_occlusion.mp4

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+oid sha256:ad233e78a5da58e971afc38a13066992fef721e3f8f2a305cbf6164bad38515a
+size 18553799

Videos_/task_tunnel.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:213b8f7c82c2e7c6d12bda5190a341950471a332dcd81315103c2ee9088cc2e7
+size 31518293

convert_to_coco.py

@@ -0,0 +1,104 @@
+import os
+import json
+import cv2
+import numpy as np
+
+# Paths
+FRAMES_DIR = "test_frame"
+GT_DIR = "GT"
+OUT_PATH = "annotations"
+os.makedirs(OUT_PATH, exist_ok=True)
+
+# Output COCO-style JSON
+out_file = os.path.join(OUT_PATH, "train.json")
+
+out = {
+    "images": [],
+    "annotations": [],
+    "videos": [],
+    "categories": [
+        {"id": 1, "name": "pedestrian"}  # You can expand with more classes if needed
+    ]
+}
+
+image_cnt = 0
+ann_cnt = 0
+video_cnt = 0
+tid_curr = 0
+tid_last = -1
+
+# Loop over sequences (one per video)
+for seq in sorted(os.listdir(FRAMES_DIR)):
+    seq_path = os.path.join(FRAMES_DIR, seq)
+    if not os.path.isdir(seq_path):
+        continue
+
+    video_cnt += 1
+    out["videos"].append({"id": video_cnt, "file_name": seq})
+
+    # Frames
+    images = sorted([f for f in os.listdir(seq_path) if f.endswith(".jpg")])
+    num_images = len(images)
+
+    for i, img_name in enumerate(images):
+        img_path = os.path.join(seq_path, img_name)
+        img = cv2.imread(img_path)
+        if img is None:
+            continue
+        height, width = img.shape[:2]
+
+        image_info = {
+            "file_name": f"{seq}/{img_name}",
+            "id": image_cnt + i + 1,
+            "frame_id": i + 1,
+            "prev_image_id": image_cnt + i if i > 0 else -1,
+            "next_image_id": image_cnt + i + 2 if i < num_images - 1 else -1,
+            "video_id": video_cnt,
+            "height": height,
+            "width": width
+        }
+        out["images"].append(image_info)
+
+    # Load GT file
+    gt_path = os.path.join(GT_DIR, seq, "gt", "gt.txt")
+    if not os.path.exists(gt_path):
+        print(f" No GT found for {seq}, skipping annotations.")
+        image_cnt += num_images
+        continue
+
+    anns = np.loadtxt(gt_path, dtype=np.float32, delimiter=",")
+
+    for i in range(anns.shape[0]):
+        frame_id = int(anns[i][0])
+        track_id = int(anns[i][1])
+        x, y, w, h = anns[i][2:6]
+        conf = anns[i][6]
+        class_id = int(anns[i][7])
+        visibility = anns[i][8] 
+
+        ann_cnt += 1
+        if track_id != tid_last:
+            tid_curr += 1
+            tid_last = track_id
+
+        ann = {
+            "id": ann_cnt,
+            "category_id": class_id,  
+            "image_id": image_cnt + frame_id,
+            "track_id": tid_curr,
+            "bbox": [float(x), float(y), float(w), float(h)],
+            "conf": float(conf),
+            "iscrowd": 0,
+            "area": float(w * h),
+        }
+        out["annotations"].append(ann)
+
+    image_cnt += num_images
+
+print(f" Loaded {len(out['images'])} images and {len(out['annotations'])} annotations.")
+
+# Save JSON
+with open(out_file, "w") as f:
+    json.dump(out, f)
+
+print(f" Saved COCO-style annotations to {out_file}")

extract_frames.py

@@ -0,0 +1,60 @@
+import os
+import cv2
+import numpy as np
+
+# Paths
+videos_dir = "test_vid"
+frames_dir = "test_frame"
+
+# Define how many frames to extract for each video (should be same in GT file last row)
+frames_to_extract = {
+    "task_day_left_turn-2024_07_31_02_12_05-mot 1.1.mp4": 1965,
+    # add more here...
+}
+
+os.makedirs(frames_dir, exist_ok=True)
+
+for video_file in os.listdir(videos_dir):
+    if video_file.lower().endswith(".mp4"):
+        video_path = os.path.join(videos_dir, video_file)
+
+        # Subdirectory name (remove .mp4)
+        subdir_name = os.path.splitext(video_file)[0]
+        subdir_path = os.path.join(frames_dir, subdir_name)
+        os.makedirs(subdir_path, exist_ok=True)
+
+        cap = cv2.VideoCapture(video_path)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        desired_frames = frames_to_extract.get(video_file, total_frames)
+
+        saved = 0
+        frames = []
+
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frames.append(frame)
+
+        cap.release()
+
+
+        if desired_frames > len(frames):
+            last_frame = frames[-1]
+            while len(frames) < desired_frames:
+                frames.append(last_frame)
+
+
+        if desired_frames < len(frames):
+            indices = np.linspace(0, len(frames) - 1, desired_frames, dtype=int)
+            frames = [frames[i] for i in indices]
+
+
+        for idx, frame in enumerate(frames):
+            frame_filename = os.path.join(subdir_path, f"frame_{idx:05d}.jpg")
+            cv2.imwrite(frame_filename, frame)
+            saved += 1
+
+        print(f"Extracted {saved} frames from {video_file} into {subdir_name}/")
+
+print(" Done extracting frames for all videos.")