RARyolov8调用zed相机实现三维测距（版本一） 1.08MB

积极向上的mr.d

资源文件列表:

zed-yolov8-main.rar 大约有602个文件
  1. zed-yolov8-main\ultralytics-main\.github\dependabot.yml 335B
  2. zed-yolov8-main\ultralytics-main\.github\ISSUE_TEMPLATE\bug-report.yml 1.32KB
  3. zed-yolov8-main\ultralytics-main\.github\ISSUE_TEMPLATE\config.yml 202B
  4. zed-yolov8-main\ultralytics-main\.github\ISSUE_TEMPLATE\feature-request.yml 746B
  5. zed-yolov8-main\ultralytics-main\.github\ISSUE_TEMPLATE\question.yml 544B
  6. zed-yolov8-main\ultralytics-main\.github\workflows\ci.yaml 2.75KB
  7. zed-yolov8-main\ultralytics-main\.github\workflows\cla.yml 684B
  8. zed-yolov8-main\ultralytics-main\.github\workflows\codeql.yaml 627B
  9. zed-yolov8-main\ultralytics-main\.github\workflows\docker.yaml 1.88KB
  10. zed-yolov8-main\ultralytics-main\.github\workflows\format.yml 547B
  11. zed-yolov8-main\ultralytics-main\.github\workflows\greetings.yml 1.98KB
  12. zed-yolov8-main\ultralytics-main\.github\workflows\links.yml 1014B
  13. zed-yolov8-main\ultralytics-main\.github\workflows\publish.yml 1.91KB
  14. zed-yolov8-main\ultralytics-main\.github\workflows\stale.yml 1.02KB
  15. zed-yolov8-main\ultralytics-main\.gitignore 1.19KB
  16. zed-yolov8-main\ultralytics-main\.pre-commit-config.yaml 960B
  17. zed-yolov8-main\ultralytics-main\Author_advice.txt 90B
  18. zed-yolov8-main\ultralytics-main\CITATION.cff 406B
  19. zed-yolov8-main\ultralytics-main\cv_viewer\tracking_viewer.py 2.72KB
  20. zed-yolov8-main\ultralytics-main\cv_viewer\utils.py 496B
  21. zed-yolov8-main\ultralytics-main\docker\Dockerfile 1.57KB
  22. zed-yolov8-main\ultralytics-main\docker\Dockerfile-arm64 1.03KB
  23. zed-yolov8-main\ultralytics-main\docker\Dockerfile-conda 732B
  24. zed-yolov8-main\ultralytics-main\docker\Dockerfile-cpu 1.09KB
  25. zed-yolov8-main\ultralytics-main\docker\Dockerfile-jetson 1.01KB
  26. zed-yolov8-main\ultralytics-main\docker\Dockerfile-python 1.05KB
  27. zed-yolov8-main\ultralytics-main\docker\Dockerfile-runner 800B
  28. zed-yolov8-main\ultralytics-main\docs\build_docs.py 2.1KB
  29. zed-yolov8-main\ultralytics-main\docs\build_reference.py 1.98KB
  30. zed-yolov8-main\ultralytics-main\docs\coming_soon_template.md 973B
  31. zed-yolov8-main\ultralytics-main\docs\en\CNAME 21B
  32. zed-yolov8-main\ultralytics-main\docs\en\guides\azureml-quickstart.md 2.89KB
  33. zed-yolov8-main\ultralytics-main\docs\en\guides\conda-quickstart.md 2.08KB
  34. zed-yolov8-main\ultralytics-main\docs\en\guides\coral-edge-tpu-on-raspberry-pi.md 2.48KB
  35. zed-yolov8-main\ultralytics-main\docs\en\guides\distance-calculation.md 1.93KB
  36. zed-yolov8-main\ultralytics-main\docs\en\guides\docker-quickstart.md 1.98KB
  37. zed-yolov8-main\ultralytics-main\docs\en\guides\heatmaps.md 3.23KB
  38. zed-yolov8-main\ultralytics-main\docs\en\guides\hyperparameter-tuning.md 3.9KB
  39. zed-yolov8-main\ultralytics-main\docs\en\guides\index.md 3.41KB
  40. zed-yolov8-main\ultralytics-main\docs\en\guides\instance-segmentation-and-tracking.md 1.99KB
  41. zed-yolov8-main\ultralytics-main\docs\en\guides\isolating-segmentation-objects.md 4.72KB
  42. zed-yolov8-main\ultralytics-main\docs\en\guides\kfold-cross-validation.md 4.68KB
  43. zed-yolov8-main\ultralytics-main\docs\en\guides\model-deployment-options.md 6.51KB
  44. zed-yolov8-main\ultralytics-main\docs\en\guides\object-blurring.md 1.94KB
  45. zed-yolov8-main\ultralytics-main\docs\en\guides\object-counting.md 2.6KB
  46. zed-yolov8-main\ultralytics-main\docs\en\guides\object-cropping.md 2.2KB
  47. zed-yolov8-main\ultralytics-main\docs\en\guides\optimizing-openvino-latency-vs-throughput-modes.md 2.34KB
  48. zed-yolov8-main\ultralytics-main\docs\en\guides\raspberry-pi.md 2.86KB
  49. zed-yolov8-main\ultralytics-main\docs\en\guides\region-counting.md 1.77KB
  50. zed-yolov8-main\ultralytics-main\docs\en\guides\sahi-tiled-inference.md 2.96KB
  51. zed-yolov8-main\ultralytics-main\docs\en\guides\security-alarm-system.md 2.54KB
  52. zed-yolov8-main\ultralytics-main\docs\en\guides\speed-estimation.md 2.32KB
  53. zed-yolov8-main\ultralytics-main\docs\en\guides\triton-inference-server.md 2.19KB
  54. zed-yolov8-main\ultralytics-main\docs\en\guides\view-results-in-terminal.md 1.93KB
  55. zed-yolov8-main\ultralytics-main\docs\en\guides\vision-eye.md 1.99KB
  56. zed-yolov8-main\ultralytics-main\docs\en\guides\workouts-monitoring.md 2.37KB
  57. zed-yolov8-main\ultralytics-main\docs\en\guides\yolo-common-issues.md 6.37KB
  58. zed-yolov8-main\ultralytics-main\docs\en\guides\yolo-performance-metrics.md 4.37KB
  59. zed-yolov8-main\ultralytics-main\docs\en\guides\yolo-thread-safe-inference.md 1.94KB
  60. zed-yolov8-main\ultralytics-main\docs\en\help\CI.md 2.34KB
  61. zed-yolov8-main\ultralytics-main\docs\en\help\CLA.md 2KB
  62. zed-yolov8-main\ultralytics-main\docs\en\help\code_of_conduct.md 2.15KB
  63. zed-yolov8-main\ultralytics-main\docs\en\help\contributing.md 2.72KB
  64. zed-yolov8-main\ultralytics-main\docs\en\help\environmental-health-safety.md 1.3KB
  65. zed-yolov8-main\ultralytics-main\docs\en\help\FAQ.md 1.35KB
  66. zed-yolov8-main\ultralytics-main\docs\en\help\index.md 1.16KB
  67. zed-yolov8-main\ultralytics-main\docs\en\help\minimum_reproducible_example.md 1.59KB
  68. zed-yolov8-main\ultralytics-main\docs\en\help\privacy.md 3.06KB
  69. zed-yolov8-main\ultralytics-main\docs\en\help\security.md 1.34KB
  70. zed-yolov8-main\ultralytics-main\docs\en\hub\api\index.md 973B
  71. zed-yolov8-main\ultralytics-main\docs\en\hub\app\android.md 3.25KB
  72. zed-yolov8-main\ultralytics-main\docs\en\hub\app\index.md 1.35KB
  73. zed-yolov8-main\ultralytics-main\docs\en\hub\app\ios.md 2.27KB
  74. zed-yolov8-main\ultralytics-main\docs\en\hub\cloud-training.md 2.16KB
  75. zed-yolov8-main\ultralytics-main\docs\en\hub\datasets.md 2.83KB
  76. zed-yolov8-main\ultralytics-main\docs\en\hub\index.md 1.87KB
  77. zed-yolov8-main\ultralytics-main\docs\en\hub\inference-api.md 3.35KB
  78. zed-yolov8-main\ultralytics-main\docs\en\hub\integrations.md 1.63KB
  79. zed-yolov8-main\ultralytics-main\docs\en\hub\models.md 2.75KB
  80. zed-yolov8-main\ultralytics-main\docs\en\hub\on-premise\index.md 973B
  81. zed-yolov8-main\ultralytics-main\docs\en\hub\projects.md 2.59KB
  82. zed-yolov8-main\ultralytics-main\docs\en\hub\quickstart.md 1.58KB
  83. zed-yolov8-main\ultralytics-main\docs\en\index.md 3.37KB
  84. zed-yolov8-main\ultralytics-main\docs\en\integrations\amazon-sagemaker.md 3.79KB
  85. zed-yolov8-main\ultralytics-main\docs\en\integrations\clearml.md 4.16KB
  86. zed-yolov8-main\ultralytics-main\docs\en\integrations\comet.md 3.51KB
  87. zed-yolov8-main\ultralytics-main\docs\en\integrations\coreml.md 3.1KB
  88. zed-yolov8-main\ultralytics-main\docs\en\integrations\dvc.md 3.62KB
  89. zed-yolov8-main\ultralytics-main\docs\en\integrations\edge-tpu.md 2.9KB
  90. zed-yolov8-main\ultralytics-main\docs\en\integrations\gradio.md 1.69KB
  91. zed-yolov8-main\ultralytics-main\docs\en\integrations\index.md 3.21KB
  92. zed-yolov8-main\ultralytics-main\docs\en\integrations\mlflow.md 2.28KB
  93. zed-yolov8-main\ultralytics-main\docs\en\integrations\ncnn.md 3.04KB
  94. zed-yolov8-main\ultralytics-main\docs\en\integrations\neural-magic.md 3.52KB
  95. zed-yolov8-main\ultralytics-main\docs\en\integrations\onnx.md 3.14KB
  96. zed-yolov8-main\ultralytics-main\docs\en\integrations\openvino.md 5.52KB
  97. zed-yolov8-main\ultralytics-main\docs\en\integrations\paddlepaddle.md 3.26KB
  98. zed-yolov8-main\ultralytics-main\docs\en\integrations\ray-tune.md 3.47KB
  99. zed-yolov8-main\ultralytics-main\docs\en\integrations\roboflow.md 5.15KB
  100. zed-yolov8-main\ultralytics-main\docs\en\integrations\tensorboard.md 3.92KB
  101. zed-yolov8-main\ultralytics-main\docs\en\integrations\tensorrt.md 3.02KB
  102. zed-yolov8-main\ultralytics-main\docs\en\integrations\tflite.md 3.13KB
  103. zed-yolov8-main\ultralytics-main\docs\en\integrations\torchscript.md 3.48KB
  104. zed-yolov8-main\ultralytics-main\docs\en\integrations\weights-biases.md 3.64KB
  105. zed-yolov8-main\ultralytics-main\docs\en\models\fast-sam.md 3.64KB
  106. zed-yolov8-main\ultralytics-main\docs\en\models\index.md 2.46KB
  107. zed-yolov8-main\ultralytics-main\docs\en\models\mobile-sam.md 2.3KB
  108. zed-yolov8-main\ultralytics-main\docs\en\models\rtdetr.md 2.42KB
  109. zed-yolov8-main\ultralytics-main\docs\en\models\sam.md 4.35KB
  110. zed-yolov8-main\ultralytics-main\docs\en\models\yolo-nas.md 2.85KB
  111. zed-yolov8-main\ultralytics-main\docs\en\models\yolo-world.md 4.13KB
  112. zed-yolov8-main\ultralytics-main\docs\en\models\yolov3.md 2.24KB
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  115. zed-yolov8-main\ultralytics-main\docs\en\models\yolov6.md 2.68KB
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  117. zed-yolov8-main\ultralytics-main\docs\en\models\yolov8.md 4.25KB
  118. zed-yolov8-main\ultralytics-main\docs\en\models\yolov9.md 4.35KB
  119. zed-yolov8-main\ultralytics-main\docs\en\modes\benchmark.md 2.69KB
  120. zed-yolov8-main\ultralytics-main\docs\en\modes\export.md 3.02KB
  121. zed-yolov8-main\ultralytics-main\docs\en\modes\index.md 1.92KB
  122. zed-yolov8-main\ultralytics-main\docs\en\modes\predict.md 10.09KB
  123. zed-yolov8-main\ultralytics-main\docs\en\modes\track.md 5.24KB
  124. zed-yolov8-main\ultralytics-main\docs\en\modes\train.md 8.21KB
  125. zed-yolov8-main\ultralytics-main\docs\en\modes\val.md 2.97KB
  126. zed-yolov8-main\ultralytics-main\docs\en\quickstart.md 5.73KB
  127. zed-yolov8-main\ultralytics-main\docs\en\reference\cfg\__init__.md 536B
  128. zed-yolov8-main\ultralytics-main\docs\en\reference\data\annotator.md 420B
  129. zed-yolov8-main\ultralytics-main\docs\en\reference\data\augment.md 550B
  130. zed-yolov8-main\ultralytics-main\docs\en\reference\data\base.md 378B
  131. zed-yolov8-main\ultralytics-main\docs\en\reference\data\build.md 454B
  132. zed-yolov8-main\ultralytics-main\docs\en\reference\data\converter.md 448B
  133. zed-yolov8-main\ultralytics-main\docs\en\reference\data\dataset.md 444B
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  137. zed-yolov8-main\ultralytics-main\docs\en\reference\data\loaders.md 479B
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  140. zed-yolov8-main\ultralytics-main\docs\en\reference\engine\exporter.md 423B
  141. zed-yolov8-main\ultralytics-main\docs\en\reference\engine\model.md 418B
  142. zed-yolov8-main\ultralytics-main\docs\en\reference\engine\predictor.md 397B
  143. zed-yolov8-main\ultralytics-main\docs\en\reference\engine\results.md 418B
  144. zed-yolov8-main\ultralytics-main\docs\en\reference\engine\trainer.md 402B
  145. zed-yolov8-main\ultralytics-main\docs\en\reference\engine\tuner.md 477B
  146. zed-yolov8-main\ultralytics-main\docs\en\reference\engine\validator.md 395B
  147. zed-yolov8-main\ultralytics-main\docs\en\reference\hub\auth.md 411B
  148. zed-yolov8-main\ultralytics-main\docs\en\reference\hub\session.md 420B
  149. zed-yolov8-main\ultralytics-main\docs\en\reference\hub\utils.md 428B
  150. zed-yolov8-main\ultralytics-main\docs\en\reference\hub\__init__.md 446B
  151. zed-yolov8-main\ultralytics-main\docs\en\reference\models\fastsam\model.md 409B
  152. zed-yolov8-main\ultralytics-main\docs\en\reference\models\fastsam\predict.md 443B
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  159. zed-yolov8-main\ultralytics-main\docs\en\reference\models\rtdetr\model.md 405B
  160. zed-yolov8-main\ultralytics-main\docs\en\reference\models\rtdetr\predict.md 417B
  161. zed-yolov8-main\ultralytics-main\docs\en\reference\models\rtdetr\train.md 435B
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  174. zed-yolov8-main\ultralytics-main\docs\en\reference\models\yolo\classify\predict.md 415B
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  540. zed-yolov8-main\ultralytics-main\ultralytics\models\yolo\obb
  541. zed-yolov8-main\ultralytics-main\ultralytics\models\yolo\pose
  542. zed-yolov8-main\ultralytics-main\ultralytics\models\yolo\segment
  543. zed-yolov8-main\ultralytics-main\docs\en\guides
  544. zed-yolov8-main\ultralytics-main\docs\en\help
  545. zed-yolov8-main\ultralytics-main\docs\en\hub
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  551. zed-yolov8-main\ultralytics-main\docs\en\usage
  552. zed-yolov8-main\ultralytics-main\docs\en\yolov5
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  557. zed-yolov8-main\ultralytics-main\examples\YOLOv8-ONNXRuntime-Rust\src
  558. zed-yolov8-main\ultralytics-main\ultralytics\cfg\models
  559. zed-yolov8-main\ultralytics-main\ultralytics\cfg\trackers
  560. zed-yolov8-main\ultralytics-main\ultralytics\data\explorer
  561. zed-yolov8-main\ultralytics-main\ultralytics\data\scripts
  562. zed-yolov8-main\ultralytics-main\ultralytics\models\fastsam
  563. zed-yolov8-main\ultralytics-main\ultralytics\models\nas
  564. zed-yolov8-main\ultralytics-main\ultralytics\models\rtdetr
  565. zed-yolov8-main\ultralytics-main\ultralytics\models\sam
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  567. zed-yolov8-main\ultralytics-main\ultralytics\models\yolo
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  569. zed-yolov8-main\ultralytics-main\ultralytics\trackers\utils
  570. zed-yolov8-main\ultralytics-main\ultralytics\utils\callbacks
  571. zed-yolov8-main\ultralytics-main\.github\ISSUE_TEMPLATE
  572. zed-yolov8-main\ultralytics-main\.github\workflows
  573. zed-yolov8-main\ultralytics-main\docs\en
  574. zed-yolov8-main\ultralytics-main\docs\overrides
  575. zed-yolov8-main\ultralytics-main\examples\YOLOv8-CPP-Inference
  576. zed-yolov8-main\ultralytics-main\examples\YOLOv8-LibTorch-CPP-Inference
  577. zed-yolov8-main\ultralytics-main\examples\YOLOv8-ONNXRuntime
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  579. zed-yolov8-main\ultralytics-main\examples\YOLOv8-ONNXRuntime-Rust
  580. zed-yolov8-main\ultralytics-main\examples\YOLOv8-OpenCV-int8-tflite-Python
  581. zed-yolov8-main\ultralytics-main\examples\YOLOv8-OpenCV-ONNX-Python
  582. zed-yolov8-main\ultralytics-main\examples\YOLOv8-Region-Counter
  583. zed-yolov8-main\ultralytics-main\examples\YOLOv8-SAHI-Inference-Video
  584. zed-yolov8-main\ultralytics-main\examples\YOLOv8-Segmentation-ONNXRuntime-Python
  585. zed-yolov8-main\ultralytics-main\ultralytics\cfg
  586. zed-yolov8-main\ultralytics-main\ultralytics\data
  587. zed-yolov8-main\ultralytics-main\ultralytics\engine
  588. zed-yolov8-main\ultralytics-main\ultralytics\hub
  589. zed-yolov8-main\ultralytics-main\ultralytics\models
  590. zed-yolov8-main\ultralytics-main\ultralytics\nn
  591. zed-yolov8-main\ultralytics-main\ultralytics\solutions
  592. zed-yolov8-main\ultralytics-main\ultralytics\trackers
  593. zed-yolov8-main\ultralytics-main\ultralytics\utils
  594. zed-yolov8-main\ultralytics-main\.github
  595. zed-yolov8-main\ultralytics-main\cv_viewer
  596. zed-yolov8-main\ultralytics-main\docker
  597. zed-yolov8-main\ultralytics-main\docs
  598. zed-yolov8-main\ultralytics-main\examples
  599. zed-yolov8-main\ultralytics-main\ogl_viewer
  600. zed-yolov8-main\ultralytics-main\ultralytics
  601. zed-yolov8-main\ultralytics-main
  602. zed-yolov8-main

资源介绍:

在本文中，我们将深入探讨如何使用YOLOv8与ZED相机协同工作，实现三维测距功能。YOLO（You Only Look Once）是一种高效的实时目标检测算法，而ZED相机则是一款高性能的立体视觉设备，能够提供精确的深度信息。在版本一的实现中，我们将结合这两者的优势，构建一个强大的三维测量系统。 YOLOv8是YOLO系列的最新版本，它在YOLOv7的基础上进行了优化，提高了目标检测的速度和准确性。YOLOv8采用了更先进的网络结构，如混合卷积层、空洞卷积和多尺度特征融合，使得模型在保持实时性能的同时，增强了对小目标的检测能力。在本项目中，YOLOv8将用于识别场景中的目标。 接着，ZED相机是来自StereoLabs的一款双目立体相机，它通过拍摄连续的图像并进行匹配来计算深度信息，从而实现三维感知。ZED SDK（软件开发工具包）提供了丰富的API和功能，可以方便地集成到各种应用中，包括机器人导航、AR/VR、3D重建等。在我们的三维测距系统中，ZED相机将提供每个目标的精确深度信息。 要将YOLOv8与ZED相机结合，我们需要完成以下步骤： 1. **环境配置**：确保安装了支持YOLOv8的深度学习框架，如TensorFlow或PyTorch，并安装ZED SDK。根据项目需求，可能还需要.NET框架支持。 2. **YOLOv8模型训练**：根据应用场景，对YOLOv8模型进行训练，使其能识别目标类别。这通常涉及数据集的准备、标注以及模型的训练过程。 3. **ZED相机初始化**：使用ZED SDK的API初始化相机，设置分辨率、帧率、深度模式等参数。确保相机与计算机连接稳定，并能成功获取图像。 4. **图像处理与目标检测**：通过YOLOv8模型对ZED相机捕获的图像进行实时处理，识别出目标物体。YOLOv8的输出将包含目标的边界框、类别概率和置信度。 5. **深度信息融合**：利用ZED SDK提供的函数获取每个目标的深度信息。这一步需要将YOLOv8的检测结果与ZED相机的深度图进行对应，计算出每个目标的三维坐标。 6. **三维测距**：根据目标的三维坐标，可以计算其距离、大小等信息。这在自动化、机器人等领域有着广泛的应用，如避障、抓取等。 7. **可视化与输出**：将二维图像与三维信息相结合，进行可视化展示。可以使用matplotlib、OpenCV或其他可视化库，显示目标的检测框及其三维位置。 在实践中，可能会遇到一些挑战，如模型的实时性、深度信息的精度以及系统整体的稳定性。需要不断优化模型、调整参数以达到最佳效果。此外，对于复杂的场景，可能还需要考虑多目标跟踪和遮挡问题。 通过结合YOLOv8的强大目标检测能力和ZED相机的精准三维感知，我们可以构建一个高效、准确的三维测距系统。这个系统的应用范围广泛，包括工业自动化、无人驾驶、无人机导航等多个领域，具有很高的实用价值。
# Multi-Object Tracking with Ultralytics YOLO <img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png" alt="YOLOv8 trackers visualization"> Object tracking in the realm of video analytics is a critical task that not only identifies the location and class of objects within the frame but also maintains a unique ID for each detected object as the video progresses. The applications are limitless—ranging from surveillance and security to real-time sports analytics. ## Why Choose Ultralytics YOLO for Object Tracking? The output from Ultralytics trackers is consistent with standard object detection but has the added value of object IDs. This makes it easy to track objects in video streams and perform subsequent analytics. Here's why you should consider using Ultralytics YOLO for your object tracking needs: - **Efficiency:** Process video streams in real-time without compromising accuracy. - **Flexibility:** Supports multiple tracking algorithms and configurations. - **Ease of Use:** Simple Python API and CLI options for quick integration and deployment. - **Customizability:** Easy to use with custom trained YOLO models, allowing integration into domain-specific applications. **Video Tutorial:** [Object Detection and Tracking with Ultralytics YOLOv8](https://www.youtube.com/embed/hHyHmOtmEgs?si=VNZtXmm45Nb9s-N-). ## Features at a Glance Ultralytics YOLO extends its object detection features to provide robust and versatile object tracking: - **Real-Time Tracking:** Seamlessly track objects in high-frame-rate videos. - **Multiple Tracker Support:** Choose from a variety of established tracking algorithms. - **Customizable Tracker Configurations:** Tailor the tracking algorithm to meet specific requirements by adjusting various parameters. ## Available Trackers Ultralytics YOLO supports the following tracking algorithms. They can be enabled by passing the relevant YAML configuration file such as `tracker=tracker_type.yaml`: - [BoT-SORT](https://github.com/NirAharon/BoT-SORT) - Use `botsort.yaml` to enable this tracker. - [ByteTrack](https://github.com/ifzhang/ByteTrack) - Use `bytetrack.yaml` to enable this tracker. The default tracker is BoT-SORT. ## Tracking To run the tracker on video streams, use a trained Detect, Segment or Pose model such as YOLOv8n, YOLOv8n-seg and YOLOv8n-pose. #### Python ```python from ultralytics import YOLO # Load an official or custom model model = YOLO("yolov8n.pt") # Load an official Detect model model = YOLO("yolov8n-seg.pt") # Load an official Segment model model = YOLO("yolov8n-pose.pt") # Load an official Pose model model = YOLO("path/to/best.pt") # Load a custom trained model # Perform tracking with the model results = model.track( source="https://youtu.be/LNwODJXcvt4", show=True ) # Tracking with default tracker results = model.track( source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml" ) # Tracking with ByteTrack tracker ``` #### CLI ```bash # Perform tracking with various models using the command line interface yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" # Official Detect model yolo track model=yolov8n-seg.pt source="https://youtu.be/LNwODJXcvt4" # Official Segment model yolo track model=yolov8n-pose.pt source="https://youtu.be/LNwODJXcvt4" # Official Pose model yolo track model=path/to/best.pt source="https://youtu.be/LNwODJXcvt4" # Custom trained model # Track using ByteTrack tracker yolo track model=path/to/best.pt tracker="bytetrack.yaml" ``` As can be seen in the above usage, tracking is available for all Detect, Segment and Pose models run on videos or streaming sources. ## Configuration ### Tracking Arguments Tracking configuration shares properties with Predict mode, such as `conf`, `iou`, and `show`. For further configurations, refer to the [Predict](https://docs.ultralytics.com/modes/predict/) model page. #### Python ```python from ultralytics import YOLO # Configure the tracking parameters and run the tracker model = YOLO("yolov8n.pt") results = model.track( source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True ) ``` #### CLI ```bash # Configure tracking parameters and run the tracker using the command line interface yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show ``` ### Tracker Selection Ultralytics also allows you to use a modified tracker configuration file. To do this, simply make a copy of a tracker config file &#40;for example, `custom_tracker.yaml`&#41; from [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) and modify any configurations (except the `tracker_type`) as per your needs. #### Python ```python from ultralytics import YOLO # Load the model and run the tracker with a custom configuration file model = YOLO("yolov8n.pt") results = model.track( source="https://youtu.be/LNwODJXcvt4", tracker="custom_tracker.yaml" ) ``` #### CLI ```bash # Load the model and run the tracker with a custom configuration file using the command line interface yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" tracker='custom_tracker.yaml' ``` For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/trackers](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/trackers) page. ## Python Examples ### Persisting Tracks Loop Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker than the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image. #### Python ```python import cv2 from ultralytics import YOLO # Load the YOLOv8 model model = YOLO("yolov8n.pt") # Open the video file video_path = "path/to/video.mp4" cap = cv2.VideoCapture(video_path) # Loop through the video frames while cap.isOpened(): # Read a frame from the video success, frame = cap.read() if success: # Run YOLOv8 tracking on the frame, persisting tracks between frames results = model.track(frame, persist=True) # Visualize the results on the frame annotated_frame = results[0].plot() # Display the annotated frame cv2.imshow("YOLOv8 Tracking", annotated_frame) # Break the loop if 'q' is pressed if cv2.waitKey(1) & 0xFF == ord("q"): break else: # Break the loop if the end of the video is reached break # Release the video capture object and close the display window cap.release() cv2.destroyAllWindows() ``` Please note the change from `model(frame)` to `model.track(frame)`, which enables object tracking instead of simple detection. This modified script will run the tracker on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'. ### Plotting Tracks Over Time Visualizing object tracks over consecutive frames can provide valuable insights into the movement patterns and behavior of detected objects within a video. With Ultralytics YOLOv8, plotting these tracks is a seamless and efficient process. In the following example, we demonstrate how to utilize YOLOv8's tracking capabilities to plot the movement of detected objects across multiple video frames. This script involves opening a video file, reading it frame by frame, and utilizing the YOLO model to identify and track various objects. By retaining the center points of the detected bounding boxes and connecting them, we can draw lines that represent the paths followed by the tracked objects. #### Python ```python from collections import defaultdict import cv2 import numpy as np from ultralytics import YOLO # Load the YOLOv8 model model = YOLO("y
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