基于BoxMOT的目标检测与跟踪全流程详解

一、技术背景与应用场景

目标检测与跟踪是计算机视觉领域的核心技术，广泛应用于智能监控、自动驾驶、运动分析等场景。BoxMOT集成了多种先进的跟踪算法（如ByteTrack、DeepOCSort等），配合YOLOv8等检测模型，可以实现高精度的实时目标跟踪。本教程将带您从零开始搭建完整的实验环境，并通过实际案例演示完整流程。

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二、环境搭建

2.1 Docker容器配置

docker run --gpus all --shm-size=32g -it \-e NVIDIA_VISIBLE_DEVICES=all --privileged --net=host \-v $PWD:/home -w /home --name boxmot nvcr.io/nvidia/pytorch:24.03-py3 /bin/bash

为什么要使用Docker？

• 保证环境一致性：避免不同系统环境导致的依赖冲突
• GPU加速支持：--gpus all参数启用NVIDIA GPU加速
• 共享内存设置：--shm-size=32g确保大内存需求应用稳定运行
• 目录映射：-v $PWD:/home将主机当前目录挂载到容器内

2.2 目录结构规划

mkdir -p /home/{workspace,third_party,models,videos,datasets}

目录结构说明：

• workspace: 存放核心代码库
• third_party: 第三方依赖组件
• models: 预训练模型存储
• videos: 测试视频素材
• datasets: 训练/验证数据集

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三、关键资源准备

3.1 数据集选择

cd /home/datasets
wget -O MOT20.zip https://motchallenge.net/data/MOT20.zip
wget -O MOT17.zip https://motchallenge.net/data/MOT17.zip
unzip MOT17.zip

MOT数据集是目标跟踪领域权威基准数据集：

• 包含密集人群、复杂场景
• 提供精确的标注信息
• 支持多目标跟踪评估

3.2 模型选择

cd /home/models
wget https://huggingface.co/spaces/xfys/yolov5_tracking/resolve/main/weights/osnet_x0_25_msmt17.pt
wget https://huggingface.co/Ultralytics/YOLOv8/resolve/main/yolov8n.pt
wget https://huggingface.co/Ultralytics/YOLOv8/resolve/main/yolov8x.pt
wget https://huggingface.co/Ultralytics/YOLOv8/resolve/main/yolov8l.pt
wget https://huggingface.co/Ultralytics/YOLOv8/resolve/main/yolov8m.pt
wget https://huggingface.co/Ultralytics/YOLOv8/resolve/main/yolov8s.pt

3.3 视频素材准备

cd /home/videos
wget https://media.roboflow.com/supervision/video-examples/vehicles.mp4
wget https://media.roboflow.com/supervision/video-examples/milk-bottling-plant.mp4
wget https://media.roboflow.com/supervision/video-examples/vehicles-2.mp4
wget https://media.roboflow.com/supervision/video-examples/grocery-store.mp4
wget https://media.roboflow.com/supervision/video-examples/subway.mp4
wget https://media.roboflow.com/supervision/video-examples/market-square.mp4
wget https://media.roboflow.com/supervision/video-examples/people-walking.mp4
wget https://media.roboflow.com/supervision/video-examples/beach-1.mp4
wget https://media.roboflow.com/supervision/video-examples/basketball-1.mp4
wget https://media.roboflow.com/supervision/video-examples/skiing.mp4

提供多种场景测试视频：

• 交通监控（vehicles.mp4）
• 工业检测（milk-bottling-plant.mp4）
• 人群分析（people-walking.mp4）

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四、核心组件安装

4.1 基础组件安装

apt update
apt install ffmpeg -y
apt install git -y
apt-get install ffmpeg libavcodec-dev libavformat-dev libswscale-dev -y
python -m pip install --upgrade pip
pip install loguru
pip install gdown
pip install ftfy
pip install lap
pip install filterpy
pip install numpy==1.24.4
pip install scikit-build
pip install gitpython

4.2 OpenCV定制编译

cd /home/third_party
git clone --recursive https://github.com/opencv/opencv-python.git
cd opencv-python/
git checkout 72
git submodule update --recursive
export ENABLE_CONTRIB=1
export WITH_FFMPEG=ON
python setup.py bdist_wheel
pip uninstall opencv opencv-contrib-python -y
rm /usr/local/lib/python3.10/dist-packages/cv2 -rf
pip install dist/*

确认OpenCV支持FFMPEG

cd /home
python3 -c "import cv2;print(cv2.getBuildInformation())"

4.3 下载BoxMOT框架,配置环境变量

cd /home/workspace
git clone https://github.com/JonathonLuiten/TrackEval.git
cd TrackEval
python3 setup.py install
cd /home/workspace
git clone --recursive https://github.com/mikel-brostrom/boxmot.git
cd boxmot
git checkout v12.0.9
mkdir tracking/val_utils -p
cp /home/workspace/TrackEval/scripts tracking/val_utils/ -rf
export PYTHONPATH=$PWD:$PYTHONPATH
cd /home/workspace
git clone https://github.com/mikel-brostrom/ultralytics.git
cd ultralytics/
git checkout 8e17ff56a9db8933a1962b88e05547dd2cce9c48
export PYTHONPATH=$PWD:$PYTHONPATH

框架主要功能模块：

1. 目标检测（YOLO系列）
2. 特征提取（ReID模型）
3. 数据关联（Kalman滤波、匈牙利算法）
4. 轨迹管理（轨迹插值、状态预测）

五、目标跟踪实战演示

cd /home/workspace
cat> main.py<<-'EOF'
import argparse
from functools import partial
from pathlib import Path
from PIL import Image
import cv2
import torch
from boxmot import TRACKERS
from boxmot.tracker_zoo import create_tracker
from boxmot.utils import ROOT, WEIGHTS, TRACKER_CONFIGS
from boxmot.utils.checks import