MOT-Tracking-by-Detection-Pipeline - For Tracking-by-Detection format MOT (Multi Object Tracking), is it a framework that separates Detection and Tracking processes?

Related tags

Deep Learning ssd multi-object-tracking mot tracking-by-detection centernet mediapipe efficientdet nanodet motpy norfair yolox bytetrack
Overview

MOT-Tracking-by-Detection-Pipeline

Tracking-by-Detection形式のMOT(Multi Object Tracking)について、
DetectionとTrackingの処理を分離して寄せ集めたフレームワークです。

09.MOT.mp4

Usage

デモの実行方法は以下です。

python main.py
  • --device
    カメラデバイス番号の指定
    デフォルト：0
  • --movie
    動画ファイルの指定 ※指定時はカメラデバイスより優先
    デフォルト：指定なし
  • --detector
    Object Detectionのモデル選択
    yolox, efficientdet, ssd, centernet, nanodet, mediapipe_face, mediapipe_hand の何れかを指定
    デフォルト：yolox
  • --tracker
    トラッキングアルゴリズムの選択
    motpy, bytetrack, norfair の何れかを指定
    デフォルト：bytetrack

Direcotry

│  main.py
│  test.mp4
├─Detector
│  │  detector.py
│  └─xxxxxxxx
│      │  xxxxxxxx.py
│      │  config.json
│      │  LICENSE
│      └─model
│          xxxxxxxx.onnx
└─Tracker
    │  tracker.py
    └─yyyyyyyy
        │  yyyyyyyy.py
        │  config.json
        │  LICENSE
        └─tracker

各モデル、トラッキングアルゴリズムを格納しているディレクトリには、
ライセンス条項とコンフィグを同梱しています。

Detector

モデル名 取得元リポジトリ ライセンス 備考
YOLOX Megvii-BaseDetection/YOLOX Apache-2.0 YOLOX-ONNX-TFLite-Sampleにて
ONNX化したモデルを使用
EfficientDet tensorflow/models Apache-2.0 Object-Detection-API-TensorFlow2ONNXにて
ONNX化したモデルを使用
SSD MobileNet v2 FPNLite tensorflow/models Apache-2.0 Object-Detection-API-TensorFlow2ONNXにて
ONNX化したモデルを使用
CenterNet tensorflow/models Apache-2.0 Object-Detection-API-TensorFlow2ONNXにて
ONNX化したモデルを使用
NanoDet RangiLyu/nanodet Apache-2.0 NanoDet-ONNX-Sampleにて
ONNX化したモデルを使用
MediaPipe Face Detection google/mediapipe Apache-2.0 目、鼻、口、耳のキーポイントは未使用
MediaPipe Hands google/mediapipe Apache-2.0 ランドマークから外接矩形を算出し使用

Tracker

アルゴリズム名 取得元リポジトリ ライセンス 備考
motpy wmuron/motpy MIT マルチクラス対応
ByteTrack ifzhang/ByteTrack MIT -
Norfair tryolabs/norfair MIT -

Author

高橋かずひと(https://twitter.com/KzhtTkhs)

License

MOT-Tracking-by-Detection-Pipeline is under MIT License.

※MOT-Tracking-by-Detection-Pipelineのソースコード自体はMIT Licenseでの提供ですが、
各アルゴリズムのソースコードは、それぞれのライセンスに従います。
詳細は各ディレクトリ同梱のLICENSEファイルをご確認ください。

License(Movie)

サンプル動画はNHKクリエイティブ・ライブラリーのイタリア ミラノの横断歩道を使用しています。

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Comments
  • : cannot connect to X server

    : cannot connect to X server

    /usr/local/lib/python3.7/dist-packages/onnxruntime/capi/onnxruntime_inference_collection.py:56: UserWarning: Specified provider 'CUDAExecutionProvider' is not in available provider names.Available providers: 'CPUExecutionProvider' "Available providers: '{}'".format(name, ", ".join(available_provider_names))) Detector: yolox Target ID: [1] GPU: True { 'class_score_th': 0.3, 'input_shape': '416, 416', 'model_path': 'Detector/yolox/model/yolox_nano_416x416.onnx', 'nms_score_th': 0.1, 'nms_th': 0.45, 'with_p6': False}

    Tracker: bytetrack FPS: 29.97 GPU: False { 'match_thresh': 0.8, 'min_box_area': 10, 'mot20': False, 'track_buffer': 30, 'track_thresh': 0.5}

    : cannot connect to X server

    Can you help me with this? I am trying to run your code in google colab on your test video.

    This is what I am trying to execute: !python main.py --movie test.mp4 --use_gpu --target_id=1 --tracker bytetrack

    opened by JovanBosic 1
Owner
KazuhitoTakahashi
KazuhitoTakahashi
GitHub
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